Digital Signal Processing 33 (2014) 98–115

Contents lists available at ScienceDirect

Digital Signal Processing

www.elsevier.com/locate/dsp

Cooperative and joint video multicast over MIMO–OFDM networks ✩

Taegeun Oh, Sanghoon Lee ∗

Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea

a r t i c l e i n f o a b s t r a c t

Article history:Available online 2 July 2014

Keywords:Cooperative video transmissionUtility maximizationAdaptive resource allocationMIMOOptimization decompositionMulticast service

Systems that broadcast/multicast over cellular networks have recently been intensively investigated. When compared to conventional terrestrial or satellite broadcasting systems, the Quality of Service (QoS) experienced by edge users is an important issue due to the inevitable inter-cell interference (ICI) that occurs within multi-cell environments. In order to resolve this issue, we have developed cooperative sub-band allocation (CSA) and CSA-joint transmission (CSA-JT) techniques that operate as a function of defined visual importance levels assigned to multi-layer videos, where the number of service users is limited over the macro/micro cell environment. To ensure that an acceptable level of video quality is delivered to edge users, an adaptive sub-band allocation scheme for layered video is designed to enhance the overall experience for all users based on maintaining program fairness. In a multiple-input/multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system, CSA can effectively mitigate ICI at the cell border via base station (BS) cooperation. Moreover, CSA-JT can improve the QoS for cell border users via joint transmission among the cooperating BSs. In order to achieve the delivery of optimal visual quality, an optimization problem is formulated that seeks to maximize the sum experience of the multicast users. A dual decomposition technique is applied in order to reduce the computational complexity of the system. Simulation results show that the CSA and CSA-JT algorithms exhibit a remarkable reduction of outage probability.

© 2014 Elsevier Inc. All rights reserved.

1. Introduction

Constantly increasing demands for wireless multimedia services have led to the development of reliable wireless networks, such as the mobile Worldwide Interoperability for Microwave Access (WiMAX) [1] and the Third Generation Partnership Project Long-Term Evolution (3GPP-LTE) [2,3]. These network platforms have primarily been optimized in order to achieve high data transmis-sion delivering satisfactory quality-of-service (QoS), that may be adequate to provide broadcast/multicast multimedia services such as mobile television. However, the resource allocation algorithms deployed in these services have been developed from the perspec-tive of point-to-point (PtP) communications, rather than point-to-multipoint (PtM) communications.

✩ This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2A10011764).

* Corresponding author.E-mail addresses: tgoh27@yonsei.ac.kr (T. Oh), slee@yonsei.ac.kr (S. Lee).

http://dx.doi.org/10.1016/j.dsp.2014.06.0151051-2004/© 2014 Elsevier Inc. All rights reserved.

In mobile WiMAX [4] and 3GPP release 6 [5,6], multicast broad-cast services (MBS) and multimedia broadcast/multicast service (MBMS) have been introduced in order to provide such a plat-form. Since several users share a common radio resource, both MBS and MBMS allow for efficient usage of radio resources. How-ever, the channel deployed by each user may differ with different throughputs. In the case of PtM transmission, it is difficult to cre-ate an optimal resource assignment for each user based on channel feedback. Indeed, we are not aware of any prior work providing a workable dynamic resource allocation protocol for each user over PtM based on the channel status of each user. In particular, a heavy burden is required in order to transmit entire video streams to the cell boundary where rapid degradation of the channel throughput often occurs due to severe inter-cell interference (ICI).

In order to resolve this problem, multiple bitstreams can be generated using Scalable Video Coding (SVC), each of which is in-dependently delivered using a flow control algorithm that operates as a function of the channel status [7]. In SVC, the original se-quence is divided into base and enhancement layers in order to represent visual data that is determined to be of higher and lower importance, respectively. By ensuring the successful delivery of the

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 99

base layer to edge users, a minimal QoS can be guaranteed, even if part of the enhancement layer’s bitstream is lost. Since the bit-stream’s enhancement layer size is much larger than that of the base layer, a more reliable level of visual quality can ostensibly be maintained by protecting the bitstream of the base layer in the face of channel errors [7,8]. Various methods for video mul-ticast with SVC are introduced to guarantee a minimal QoS and to improve the visual quality via bandwidth, power time or code allocation for each layer [9–12].

Most previous work on video multicast has focused on QoS improvement and visual quality within a single-cell environment. However, when the video data is transmitted over a multi-cell environment, the base stations (BSs) interfere with each other. Since these ICIs aggravate the QoS of the cell border users, it becomes necessary to develop an efficient resource management technique for mitigating the ICI [13]. Recently, resource man-agement schemes have been studied in order to improve both QoS and capacity (particularly at the cell border) over orthog-onal frequency-division multiplexing (OFDM)-based wireless net-works [14–17]. However, it is difficult to find resource manage-ment schemes for multicast service, particularly for video appli-cation, even if they are able to enhance the QoS of the unicast services. Therefore, radio resource allocation techniques need to be developed for video multicast services in order to effectively im-prove the QoS.

Previous research has shown that each BS could provide ser-vices using a bandwidth not shared by adjacent cells in order to avoid ICI [18]. Even if a fixed resource can provide enhanced QoS and low ICI at the cell boundary, this work has poor spectral ef-ficiency and it is difficult to adjust the bandwidth according to the number of serving programs. Recently, the approaches based on the cooperation with adjacent transmitters have been intro-duced to provide a higher visual quality to edge-users [19,20]. These schemes are proposed in cooperation with relays in order to expand the coverage within the home cell. However, it is more im-portant to reduce the ICI to guarantee a minimal QoS of cell border users. Towards this end, adaptive resource allocation using cooper-ative techniques with adjacent BSs would be a promising solution in order to suppress ICI over a multi-cell environment.

Multiple-input/multiple-output (MIMO) is a major technology used for enhancing system performance via the improvement of spectral efficiency using multiple antennas for both the transmit-ters and receivers [21]. The channel capacity of MIMO networks re-lies on the design of the precoder as well as the decoder schemes designated to a point-to-point link. Therefore, it is difficult to apply them in multiuser applications where users simultaneously receive the same data. For multicast/broadcast networks, the throughput of cell border users could be improved by employing a decoder such as a zero-forcing (ZF) receiver. Nevertheless, it is necessary to de-velop resource allocation mechanisms for multi-cell environments in order to prohibit throughput degradation from the ICI.

Coordinated multiple point (CoMP) transmission and reception, also called collaborative MIMO, network MIMO, and so on, has been proposed as a solution for guaranteeing the QoS delivered to cell border users [22–25]. The CoMP technique can be categorized into two subtypes: Joint Processing/Transmission (JPT) and Coor-dinated Scheduling/Beamforming (CSB). In JPT schemes, multiple BSs share both the channel information and the data of cell border users in order to change the interfering signal into a desired signal [26]. In CSB schemes, multiple BSs share the channel information of cell border users in order to mitigate ICI [27]. Through CoMP, ICI can effectively be reduced for cell border users, but a high-speed wired backhaul is required to enable the sharing of the necessary information between multiple BSs.

We develop two resource allocation algorithms for scalable video multicast over a cooperative network, wherein cooperative

sub-band allocation (CSA) and CSA-joint transmission (CSA-JT) are accomplished as a function of the user distribution and the chan-nel information. In the proposed schemes, a sub-band for the base layer is adaptively allocated to avoid ICI as well as to enhance the QoS based on BS cooperation. Currently, CoMP is primarily targets PtP communications. Here, we explore an efficient way to extend CoMP for PtM communications by adaptively allocat-ing sub-bands via CSA and CSA-JT. Moreover, the use of CSA-JT can enhance the QoS delivered to cell border users by receiving signals from adjacent BSs over the CoMP region. However, oc-casionally, the QoS of signals delivered from adjacent BSs may be reduced because of the channel condition. To achieve efficient mode switching of joint transmission over MIMO–OFDM networks, each user determines which signal is to be used as the receive signal from either the home BS signal or the joint-transmission signal while measuring the signal quality of the ZF receiver. To fur-ther improve the visual quality, each home BS dynamically and independently assigns residual resources to the enhancement layer from other BSs. A different bandwidth can be assigned to each program by relying on the measured channel conditions of the users. Moreover, by applying optimal carrier loading ratio control, ICI can be mitigated in the sense of a reduced collision proba-bility, as a function of the signal to interference-plus-noise ratio (SINR).

2. System overview

Fig. 1 shows a service scenario of video multicast over anMIMO–OFDM network. It is assumed that a number of users are distributed over the network and each user receives a multicast video program from the BSs over a shared radio sub-band. The video program is encoded using a layered coding scheme in terms of the base and enhancement layers, and then transmitted to users. Fig. 1 shows that two users are located in the inner and outer regions of BS 1 and reconstruct the same video program with dif-ferent quality as a function of the channel condition. In general, the inner user entertains high quality video by receiving both lay-ers, while the outer user may experience quality degradation due to ICI. However, at the very least, the base layer is delivered as a QoS guarantee.

This paper proposes a radio resource allocation scheme that ex-ploits BS cooperation in order to improve the QoS of each video program, particularly at the cell boundary. Each BS of the CoMP re-gion can interchange resource allocation status with other BSs via the wired backhaul. The shared sub-band is effectively allocated using user feedback information, such as the program index and the channel condition. The feedback and allocation are periodically updated in accordance with changes in the user channels.

2.1. CSA for the base layer

In order to improve the QoS, the proposed scheme adaptively assigns the sub-band bandwidth and loading ratio for the base and enhancement layers via a two-stage process. First, sub-band allo-cation is performed for the base layer transmission. In order to mitigate ICI effectively via BS cooperation, an assumption is made where each cell consists of three 120 degree sectors and the radio resource is cooperatively allocated to the base layer of each pro-gram over the CoMP region, which is composed of three adjacent sectors, as shown in Fig. 1.

Resources for the base layer are allocated by two different methods. The first method, CSA, assigns different sub-bands to each BS over the CoMP region in order to avoid ICI, as shown in Fig. 2(a). Using channel feedback information, the three BSs over the CoMP region collaboratively allocate each sub-band. It is nec-essary that each BS selects a different sub-band for each program

100 T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115

Fig. 1. A scenario of the proposed scalable video multicast system.

Fig. 2. Base layer transmission over the CoMP region (a) with non-overlapped sub-band allocation and (b) joint transmission.

that is not being used by the other two BSs. Since each sub-band is not shared in the CoMP region of the three sectors for each pro-gram, the QoS of the base layer can be significantly improved by using a full loading ratio of 1 at the cell border while avoiding the most fatal ICIs from adjacent BSs.

The second method, CSA-JT, improves the QoS of cell border users by jointly transmitting the same signals from the BSs over the CoMP region, as shown in Fig. 2(b). In the scheme, a non-overlapped sub-band is uniquely assigned to each program via BS

cooperation, so that all the BSs over the CoMP region simultane-ously transmit the signal using the same sub-band. The cell border user can improve the QoS of the desired signal by receiving sig-nals from additional BSs. However, transmission using multiple BSs may increase ICI in other CoMP regions. Therefore, in the proposed scheme, program p is jointly transmitted when the channel capac-ity is less than the following threshold:

ri < rth, for any k ∈ Ki,p,

c,k

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 101

Table 1System notations.

U i,pk The utility function is the amount of visual information

transmitted to user k, who is receiving program p in BS i.P i The number of multicast programs in BS i.Ki,p , |Ki,p | The set and user number of the receiving program p in BS i.Ni,p

b , Ni,pe The number of allocated carriers for the base (b)

and enhancement (e) layers of program p in BS i.ρ

i,pb , ρ

i,pe The loading ratio of carriers for the base and enhancement

layers of program p in BS i.Nsc The total number of carriers for the multicast system.

C, |C| The set and number of sub-bands for the multicast system.

Cb , Ce The sub-band sets for the base and enhancement layers.

Ci,pl The sub-band set allocated to layer l of program p in BS i.

ψ ic , |ψ i

c | The set and number of carriers included with sub-band cin BS i.

where ric,k is the average channel capacity of sub-band c for user k

in BS i, and rth is the threshold value required for the joint trans-mission decision.

Moreover, in order to improve the receive channel gain due to joint transmission, we present an MIMO-based network that can demodulate program p using a receive weight vector. Each user does a comparison with the anticipated channel gains from the home BS and the cooperative BSs over the CoMP region, then de-termines the receive weight vector needed in order to obtain a higher channel gain. The resource allocation procedure for the base layer is described in detail in Section 4.1.

2.2. CSA for the enhancement layer

Residual sub-band allocation is performed for transmission of the enhancement layer. Since the sub-band of each program is ex-clusively used among the adjacent BSs over the CoMP region, most sub-bands are occupied for the base layers. Therefore, adjacent BSs can utilize relatively narrow sub-bands for the enhancement lay-ers, then independently assign them to each program, even if a carrier collision occurs among the other BSs. This ICI may disturb the delivery of the enhancement layer, particularly to the cell bor-der. A lower loading ratio can reduce the collision probability of a carrier, but a wider bandwidth is required in order to maintain a target data rate. However, it may be implausible to assign a band-width wide enough to meet the enhancement layer’s QoS for each program. Hence, it is necessary to employ an adaptive allocation scheme to improve the overall system QoS.

Here, we propose to improve the overall system QoS by calcu-lating the bandwidth and the loading ratio as a function of the user distribution of entertaining programs. In order to create our method of optimal resource allocation, we define an optimiza-tion problem, the solution of which achieves a fair allocation of bandwidth to each program based on the user distribution. The necessary notation is defined in Table 1. In BS i, the enhancement layer is transmitted using the residual resources (Nsc −∑P i

p=1 Ni,pb ).

This assignment of bandwidth is done to achieve fairness as well as to ensure program quality. In order to measure the efficiency of the assigned resource, the utility function U i,p

k is defined as the amount of visual information contained in program p of BS i, which is delivered to user k as a function of the channel capac-ity (see Section 3.1). Then, the bandwidth and the loading ratio are determined for the enhancement layer of each program. To prevent QoS degradation due to excessive allocation for a specific program, the resource should be allocated while considering fairness among programs. Finally, the objective function is designed based on a weighted proportional fairness [28]. The optimization problem is defined as

maxNi

e

P i∑p=1

∣∣Ki,p∣∣ ln

(∑k∈Ki,p U i,p

k

|Ki,p|)

(1)

subject toP i∑

p=1

Ni,pe ≤ Nsc −

P i∑p=1

Ni,pb , (2)

where Nie = {Ni,p

e | p = 1, · · · , P i} is a set of allocated carrier num-bers for 1 ≤ p ≤ P i . The procedure for solving Problem (1) is de-scribed in Section 4.2.

3. Utility function and link capacity analysis

3.1. Definition of utility function

One of our main goals is to further the design of algorithms for multimedia communication that take into account the perceptual properties of the ultimate receiver, which is the eye–brain system of human observers. Models of visual perception are increasingly being recognized as important ingredients in algorithms involved in the automatic acquisition, transmission, processing, and display of visual information. Recent work has focused on assigning visual importance to the spatio-temporal regions of images and videos in order to create perceptually relevant strategies for image compres-sion and transmission [29]. For example, the contrast sensitivity function (CSF) has been utilized in a variety of ways in the design of visual algorithms [30–33].

H.264 Scalable Video Coding (SVC) has been introduced as pro-vide an effective solution for video transmission services such as broadcast and multicast [8,34]. The term “scalability” refers to the removal of parts of the video bitstream in order to adapt it to the various needs or preferences of end users depending on link capa-bility or network conditions [34]. By the SVC, a high-quality video is encoded into one or more subset bitstreams from a base to en-hancement layers. SVC enables that the video can be reconstructed from the base to enhancement layers in hierarchical way using a partial of bitstreams. The subset of bitstreams provides lower spa-tial resolution (picture size), lower temporal resolution (frame rate) or quality (SNR) degradation when only the base layer is induced. In this paper, spatial scalability is adopted in order to support mul-tiple resolution devices. Using spatial scalability, the resolutions can be increased when adding the visual information of being de-livered by additional enhancement layers. Here, only two layers including the base and one enhancement layers are considered for simplicity of description of the algorithm. Due to the overhead of SVC, the coding efficiency is less than that of the single layer cod-ing (around 10%) [34]. However, in [15,35], we demonstrated that SVC can provide better visual quality up to 1.4 dB in PSNR at the cell border over a multi-cell environment in spite of severe ICI.

The term visual entropy is defined as the estimated number of bits needed to visually represent a coding unit. Multiplying the appropriate visual weights by the generated bit rate, the visual en-tropy becomes

Base layer: rb = wb · rb

Enhancement layer: re = we · re, (3)

where rb and re are the generated bit rates of the base and en-hancement layers, and wb and we are the visual weights of the base and enhancement layers, respectively. For example, the visual weights can be derived from the CSF [15]. Figs. 3(a) and 3(b) are the base and enhancement layer pictures using a cut-off frequency of 0.10. Although Figs. 3(a) and 3(b) have similar data rates, these pictures have different visual entropies owing to the assignment of different visual weights. Observe that the base layer consisting

102 T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115

Fig. 3. Example of scalable coded video on the “Football” sequence using a cut-off frequency of 0.10. (a) Base layer with a quantization parameter of 24, visual weight of 0.892, data rate of 35,419 bits and visual entropy of 31,593.75. (b) Enhancement layer with a quantization parameter of 36, visual weight of 0.108, data rate of 34,942 bits and visual entropy of 3773.74.

of lower frequency components maintains more visual informa-tion.

Let V = min(NT , NR) be the number of spatial sub-channels for a carrier where NT and NR represent the number of antennas for the transmitter and the receiver. Using the Shannon–Hartley the-orem, the channel capacity for layer l of program p of user k in BS i is

ri,pk,l =

∑n∈ψ i

c

V∑v=1

BWsc log2(1 + Γ i

n,k,v

), (4)

where Γ in,k,v is the SINR for spatial sub-channel v of carrier n at

user k in BS i, ψ ic is the set of carriers in sub-band c, which is allo-

cated to layer l of program p in BS i, and BWsc is the bandwidth of the carrier. Using (3) and (4), the visual entropy over the channel is

ri,pk,l = wl · ri,p

k,l . (5)

Compared to the visual entropy attained from the channel ca-pacity in (5), it is also bounded by the real transmitted data from BS i in terms of the resource used and the modulation method as follows:

ri,pl = wlBWscNi,p

l ρi,pl mV , (6)

where m is the modulation order (e.g., QPSK = 2, BPSK = 1).The term utility is defined as the visual entropy experienced by

the user as follows:

U i,pk = min

{ri,p

k,b, ri,pb

}+ min{

ri,pk,e , ri,p

e}. (7)

The first and second terms represent the amount of visual data delivered to user k for the base and enhancement layers, respec-tively.

Fig. 4 depicts the difference between the visual entropies in (5)and (6) and the utility in (7) as a function of the distance from the BS in a single-cell environment. The visual entropy over the chan-nel at the cell border decreases as the amplitude of the received signal decreases. By contrast, the user near the BS can experience higher visual entropy. However, no user is able to experience more visual information than what is transmitted from the BS.

3.2. Link analysis for the MIMO–OFDM system

3.2.1. Carrier collision probabilityThe carrier selection probability for layer l of program p in

BS i is

Fig. 4. An example of the visual entropy and utility according to the normalized distance.

f i,pl = N i,p

l

Ni,pl

, (8)

where N i,pl is the number of carriers used for layer l of program p

in BS i. Additionally, f j,pl is the carrier selection probability of an

adjacent BS j for the corresponding sub-band to layer l of program p in BS i.

Since the carrier selection for each BS is independent of other BSs within an MIMO–OFDM system, the collision probability is rep-resented as the product of the conditional probability that a carrier will be selected by the home BS and the probability that the same carrier will be selected by adjacent BSs. The collision probability of the carrier between BSs i and j for layer l of program p in BS ithen becomes

fcol = f j,pl · f i,p

l

f i,pl

· f i,pl = f j,p

l · f i,pl . (9)

3.2.2. System modelThe block diagram of the MIMO–OFDM multicast system model

is shown in Fig. 5. Here, sin = [si

n,1 · · · sin,min(NT ,NR )

]T is the symbol vector of carrier n in BS i, yi

n,k is the received signal of user k of carrier n in BS i, Hi is the channel matrix of carrier n for user

n,k

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 103

Fig. 5. Block diagram of the MIMO–OFDM multicast system.

k in BS i, nn,k is the noise vector, and ric,k is the average channel

capacity of sub-band c for user k in BS i. Let I be the set of BSs involved in the joint transmission and Ji,n be the set of BSs that simultaneously utilize carrier n, except for BS i, i.e., BS i is not an element in Ji,n . Therefore, the signals from the BSs in Ji,n inter-fere with the signal of user k over carrier n. The received signal of user k of carrier n in BS i is

yin,k =

⎧⎪⎪⎨⎪⎪⎩∑

i′∈I Hi′n,ksi

n +∑j∈Ji,n

H jn,ks j

n + nn,k,

for joint transmission;

Hin,ksi

n +∑j∈Ji,n

H jn,ks j

n + nn,k, otherwise,

(10)

where H jn,k is the channel matrix of carrier n between user k and

an adjacent BS j. The element of Hin,k is defined as

h =√

d−β

i,k · z, (11)

where di,k is the distance to user k in BS i, β is the path-loss exponent and z is a complex Gaussian random variable with zero mean and unit variance.

The demodulated signal with a zero forcing receive weight vec-tor is obtained by [21]

rin,k,v yi

n,k

=

⎧⎪⎪⎪⎪⎨⎪⎪⎪⎪⎩

∑i′∈I ri

n,k,v Hi′n,ksi

n +∑j∈Ji,n

rin,k,v H j

n,ks jn + ri

n,k,v nn,k,

for joint transmission;

rin,k,v Hi

n,ksin +∑

j∈Ji,nri

n,k,v H jn,ks j

n + rin,k,v nn,k,

otherwise,

(12)

where the receive weight matrix is Win,k = [wi

n,k,1T · · ·

win,k,min(NT ,NR )

T ]T = (Hin,k

H Hin,k)

−1Hin,k

H , and the receive weight vector is ri

n,k,v = win,k,v/‖wi

n,k,v‖ where Hin,k =∑

i′∈I Hi′n,k is used

for joint transmission, otherwise, Hin,k = Hi

n,k is used.

The SINR of spatial sub-channel v becomes

Γ in,k,v = Si,k

d

Si,kJ

= ‖rin,k,v Hi

n,k‖2|sin,v |2∑

j∈Ji,n‖ri

n,k,v H jn,k‖2|s j

n,v |2 fcol + N0

, (13)

where sin,v is the symbol of the spatial sub-channel v of carrier

n of BS i, Si,kd is the power of the received signal and Si,k

d is the power of the sum of interference and noise.

3.3. Outage probability for the MIMO–OFDM system

3.3.1. Outage probability for CSAThe outage probability of spatial sub-channel v of carrier n for

user k in BS i is defined as

F out,CSAi,k,n,v = F

[(Eb

I0

)i,k

n,v< δ

]= F

[Γ i

n,k,v <Rsc

BWscδ

], (14)

where δ is the target Eb/I0 and (Eb/I0)i,kn,v = Γ i

n,k,v BWsc/Rsc , where BWsc and Rsc are the bandwidth and the bit rate of a carrier, re-spectively.

In order to obtain the outage probability in (14), let the proba-bility density function (pdf) of a random variable S for the power be modeled as [36]

f S(s) = 1

Sexp

(− s

S

), s ≥ 0, (15)

where S = E[S] is the mean of the signal power S . In order to simplify, let Sd and S J be Si,k

d and Si,kJ , in (13), respectively.

Using (15), the cumulative distribution function (cdf) of the outage probability in (14) is obtained by the following procedure [37], denoted as follows:

X � Sd

S J, 0 ≤ X < ∞

Y � S J , 0 ≤ Y < ∞. (16)

The joint pdf of the two random variables X and Y is

f X,Y (x, y) � f Sd,S J (sd, s J )

∣∣∣∣∂(sd, s J )

∂(x, y)

∣∣∣∣. (17)

Using the Jacobian transform and the independence of the random variables X and Y , the following equation is obtained:

f X,Y (x, y) = f Sd (xy) f S J (y)y. (18)

The pdf of X can then be expressed as

f X (x) =∞∫

0

f Sd (xy) f S J (y)ydy. (19)

Finally, the outage probability in terms of the cdf becomes

F out,CSAi,k,n,v = F X

(xth)=

xth∫0

dx

∞∫0

f Sd (xy) f S J (y)ydy, (20)

where xth = RscBWsc

δ. Using (20), the outage probability for CSA can be represented.

3.3.2. Outage probability for CSA-JTFor CSA-JT, the home BS makes a joint transmission decision by

relying on the user channel quality with a threshold reference. If the signal power from the home BS is less than a threshold Sth , the joint transmission occurs with the following probability:

f jt(k) = F[

Si,kd (i) < Sth]

=Sth∫

0

1

S i,kd (i)

exp

(− 1

S i,kd (i)

sd

)dsd

= 1 − exp

(− 1

S i,kd (i)

Sth)

, (21)

where Si,kd (i) is the signal power from the home BS (BS i) for

user k, and S i,k(i) is the mean of Si,k

(i).

d d

104 T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115

Otherwise, the non-joint transmission occurs with a probabil-ity of

fnjt(k) = F[

Si,kd (i) ≥ Sth]

= exp

(− 1

S i,kd (i)

Sth)

. (22)

However, for the multicast application, it is evident that the joint transmission of a program occurs via the request of other users in addition to user k. Therefore, it is important to account for the probabilities of other users as well.

For simplicity, we assume that the cell is circular and the users are uniformly distributed. Since the home BS performs joint trans-mission even when one of the multicast users has a signal power less than the threshold, the joint transmission probability caused by other users is

f jt =∫ 1

0 2πk · F [Si,kd (i) < Sth]dk∫ 1

0 2πk dk

= 1

π

1∫0

2πk

Sth∫0

1

S i,kd (i)

exp

(− 1

S i,kd (i)

sd

)dsddk

= 1

π

1∫0

2πk

[1 − exp

(− 1

S i,kd (i)

Sth)]

dk. (23)

For other users, the probability of non-joint transmission is then

fnjt = 1 − f jt. (24)

From (21)–(24), we consider all possibilities for user k as well as other users by

f jt(k) = f jt(k) + fnjt(k) · f jt (25)

fnjt(k) = fnjt(k) · fnjt, (26)

where f jt(k) and fnjt(k) are the probability of the joint transmis-sion and non-joint transmission over the home cell, respectively.

Once the home BS determines a joint transmission for the pro-gram with the adjacent BSs over the CoMP region, each user deter-mines which signal is to be used as the receive signal from either the home BS signal or the joint-transmission signal. Since CSA-JT is based on the MIMO network, each user can select the receive sig-nal by constructing the ZF receiver in accordance with the channel between the BSs and the user.

Let Si,kd (I) be the signal power through the joint transmis-

sion from the BS set I over the CoMP region. The probability that the joint-transmission signal’s amplitude is larger than that of the home BS signal becomes

f Ijr (k) = F[

Si,kd (I) ≥ Si,k

d (i)]= F

[Si,k

d (I)

Si,kd (i)

≥ 1

], (27)

where Si,kd (I) = ‖ri

n,k,v Hin,k‖2|si

n,v |2. Using (16)–(20), the cdf of (27) can be rewritten as

f Ijr (k) =∞∫

1

dx

∞∫0

fSi,k

d (I)(xy) f

Si,kd (i)

(y)ydy. (28)

Conversely, the opposite probability is

f ijr(k) = F

[Si,k

d (I) < Si,kd (i)

]

=1∫

dx

∞∫f

Si,kd (I)

(xy) fSi,k

d (i)(y)ydy, (29)

0 0

which represents the probability that user k receives the home BS signal. Finally, the outage probability for CSA-JT is expressed

F out,CSA-JTi,k,n,v = fnjt(k) · F out,njt

i,k,n,v (i)

+ f jt(k) · [ f Ijr (k) · F out,jti,k,n,v(I) + f i

jr(k) · F out,jti,k,n,v(i)

].

(30)

The first term represents the outage probability for the non-joint transmission where F out,njt

i,k,n,v (i) = F out,jti,k,n,v(i) = F out,CSA

i,k,n,v in (20). The second and third terms represent the outage probabilities of the joint transmission where user k receives the program from the BS set I and BS i. The outage probability F out,jt

i,k,n,v (I) is obtained by a procedure similar to (16)–(20):

F out,jti,k,n,v(I) =

xth∫0

dx

∞∫0

fSi,k

d (I)(xy) f S J (y)ydy. (31)

4. Proposed cooperative sub-band allocation

4.1. BS cooperation-based sub-band allocation for the base layer

The proposed resource management algorithm assigns band-width and loading ratio for the base and enhancement layers as a function of the user distribution, including the channel informa-tion and the received program index. Fig. 6 shows the procedure for the proposed base layer resource allocation. CSA and CSA-JT assign resources to each program via Step 2 and Step 4, respec-tively.

Step 1. Feedback information: As shown in Fig. 6(a), each user feeds back to the program index as well as to the channel infor-mation of the home BS. If the channel information for all carriers is fed back, more performance gain can be achieved through effi-cient selection of sub-bands. However, the channel feedback for all the carriers of adjacent BSs may increase the system load because it requires PtP communications between the BS and its users. In order to reduce the system load caused by the feedback, the rep-resentative channel values are reported by sending the received program index and the average channel capacity of the carriers for each sub-band. The average channel capacity of sub-band c for user k in BS i is obtained by

ric,k = 1

|ψ ic|∑n∈ψ i

c

V∑v=1

log2(1 + Γ i

n,k,v

). (32)

Then, each BS arranges the average channel capacity for all Kusers as follows:

Ri =⎡⎢⎣

ri1,1 . . . ri|C|,1...

. . ....

ri1,K . . . ri|C|,K

⎤⎥⎦ , (33)

where K is the number of users in the BS (K =∑P i

p=1 |Ki,p|). The rows and columns of Ri are ordered along with the indices of users and sub-bands.

Step 2. Select sub-band c for program p: As shown in Fig. 6(b), each BS assigns a non-overlapped sub-band to each program via BS cooperation. In order for this cooperation to occur, information exchange among the BSs over the CoMP region via the wired back-haul is necessary. Amongst all programs served for users in the CoMP region, resources are first allocated to each program depend-ing on the number of service users.

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 105

Fig. 6. The procedure for the proposed base layer resource allocation. CSA consists of Step 1 and Step 2, while CSA-JT consists of Step 1 through Step 4.

Moreover, the cell border users receive distorted visual data due to the attenuation of channel capacity. In order to maintain the QoS requirement for cell border users, a sub-band is allocated to the base layer of each program having the lowest channel gain by one of the service users with high priority. From the set of candi-date sub-bands C , a user is found to have the minimum average channel capacity, kmin

c , for sub-band c by

kminc = arg min

k∈Ki,pRi(Ki,p, c

), c ∈ C, (34)

where Ri(Ki,p, c) is the submatrix consisting of the user row set Ki,p and the column of sub-band c in Ri . The user having a min-imum channel capacity for each sub-band c can differ depending on the channel condition.

After this procedure, a user having the lowest channel capacity is determined for each sub-band. The sub-band with the largest channel capacity is assigned to program p, as follows:

ci,p = arg maxc∈C

{ri

c,kminc

∣∣ c ∈ C}, (35)

where ci,p is the sub-band for program p of BS i. The BS i then sends out a notification indicating to other BSs over the CoMP region that sub-band ci,p is assigned to program p to achieve non-

overlapped resource allocation. Until the sub-bands are assigned to all the programs entertained by users in the CoMP region, Step 2 is repeated.

CSA assigns the resources to the base layers via Step 1 and Step 2. For CSA-JT, additional steps (Step 3 and Step 4) are re-quired for the joint transmission decision and the receive weight vector decision.

Step 3. Determine the joint transmission: In order to improve the QoS at the cell border, each BS determines whether or not pro-gram p is jointly transmitted with reference to the rth , as shown in Fig. 6(c). The BS i requests joint transmission to the other BSs over the CoMP region for program p if ri

ci,p ,k< rth , for any

k ∈Ki,p .Step 4. Determine the receive weight vector: Finally, each user de-

termines the receive weight vector for the ZF receiver when the program is jointly transmitted, as shown in Fig. 6(d). The channel gains from the home BS and the cooperative BSs over the CoMP region are estimated using (13) with Hi

n,k and ∑

i′∈I Hi′n,k . Each

user then determines the receive weight vector in order to obtain a higher channel gain. The user selects the receive weight vector with

∑i′∈I Hi′

n,k if the channel gain of the cooperative BSs is larger than that of the home BS, and Hi is used otherwise.

n,k

106 T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115

In summary, we have the following algorithm:

Algorithm 1 (Sub-band allocation for the base layer via BS cooperation).

1. Feedback information: Users feed back the representative chan-nel values to the home BS including the received program in-dex and the average channel capacity of sub-bands using (32).

2. Sub-band allocation: Via BS cooperation, the BSs allocate non-overlapped sub-bands to each program depending on the low-est channel gain among the service users using (34) and (35). (Step 2 is repeated until sub-bands are assigned to all the pro-grams.)

3. Joint transmission: Each BS determines whether each program is transmitted jointly in cooperation with other BSs.

4. Decision of the receive weight vector: The service users determine whether they receive signals transmitted jointly from BSs over the CoMP region or only a signal from home BS by determin-ing the receive weight vector of (12). �

4.2. Sub-band and loading ratio allocation for the enhancement layer

The BSs of the CoMP region allocate residual sub-bands, which are not assigned to the base layer, for enhancement layer transmis-sion. The enhancement layer transmission is primarily performed for users with good channel quality near the BS. Since users tend to have lower ICI near the BS, the BSs share common sub-bands and independently transmit their own enhancement layers over the sub-bands. Additionally, to improve the QoS of the cell bor-der users, ICI is mitigated using loading ratio control. To reduce the computational complexity, each BS independently determines the bandwidth and the loading ratio based on distributed manner of resource decision rather than using BS cooperation.

The utility function is controlled by the choice of bandwidth of the enhancement layer. From (1) and (7), the bandwidth for the enhancement layer is determined such that the sum of utilities for all users is enhanced based on program fairness.

The solution for Problem (1) can be found by direct com-putation over the feasible region. However, this process involves high computational complexity when assigning the residual car-

riers (Nsc − ∑P i

p=1 Ni,pb ) for program p of BS i. Thus, we utilize

a dual decomposition technique in order to reduce the computa-tional complexity. After the base layer resource allocation is com-plete (Section 4.1), the utility U i,p

k becomes the function of the carrier, U i,p

k (Ni,pe ). Since the function ln(·) of Problem (1) is a con-

cave function of the utility, U i,pk (Ni,p

e ), Problem (1) is a weighted sum of concave functions. Thus, Problem (1) is also a concave func-tion of the carrier, Ni,p

e [38]. Since Problem (1) is concave, the optimal solution can be obtained using convex optimization. The original problem is decomposed into several sub-problems, each of which determines the optimal bandwidth for the enhancement layer of each program [38].

First, relax the constraint in (1) by using a dual variable λ, thus obtaining the Lagrangian [39]:

Lag(Ni

e, λ)�

P i∑p=1

∣∣Ki,p∣∣ ln

(∑k∈Ki,p U i,p

k (Ni,pe )

|Ki,p|)

− λ

[P i∑

p=1

Ni,pe −

(Nsc −

P i∑p=1

Ni,pb

)], (36)

where Nie is the vector variable and λ is the Lagrange multi-

plier, or dual variable. The variable λ can be interpreted as the shadow price for the constrained bandwidth, Nsc − ∑P i

p=1 Ni,p .

b

Here, U i,pk (Ni,p

e ) is the estimated utility of user k receiving pro-gram p from BS i. Due to system overload from feedback, the SINR and the utility are estimated using the approximate distance of user k, which is estimated using (33).

Γ in,k,v = |si

n,v |2 · d−β

i,k∑j∈Ji,n

|s jn,v |2 · d−β

j,k fcol + N0

, (37)

where N0 is the power spectral density of the additive white Gaus-sian noise. We assume that the user distribution is symmetric in other cells where fcol = f i,p

l · f i,pl . Then, the estimated utility

U i,pk (Ni,p

e ) is obtained by substituting (37) into (4)–(7).The following sub-problem is solved for each program as fol-

lows:

Ni,p∗e (λ) = arg max

Ni,pe

[∣∣Ki,p∣∣ ln

(∑k∈Ki,p U i,p

k (Ni,pe )

|Ki,p|)

− λNi,pe

].

(38)

The master dual problem is

minλ≥0

gi(λ) �P i∑

p=1

gi,p(λ) + λ

(Nsc −

P i∑p=1

Ni,pb

)

subject to λ ≥ 0, (39)

where gi,p(λ) is the maximum value of (38) for given values of λand Ni,p∗

e (λ), and can be written as

gi,p(λ) = ∣∣Ki,p∣∣ ln

(∑k∈Ki,p U i,p

k (Ni,p∗e (λ))

|Ki,p|)

− λNi,p∗e (λ). (40)

Since the dual function gi(λ) is generally differentiable, (39)can be solved using subgradient search, as follows:

λ(t + 1)

= max

{0, λ(t) + α(t)

(P i∑

p=1

Ni,p∗e

(λ(t)

)−(

Nsc −P i∑

p=1

Ni,pb

))},

(41)

where t is the iteration index and α(t) > 0 is a sufficiently small positive step-size at iteration t . The dual variable λ(t) will converge to the dual optimal λ∗ as a → ∞. Additionally, since the duality gap for Problem (1) is zero and the solution to (38) has converged, the primal variable Ni,p∗

e (λ(t)) will also converge to the primal op-timal variable Ni,p∗

e .In summary, we have the following algorithm:

Algorithm 2 (Sub-band allocation for the enhancement layer).

1. Initialization: Set t = 0 and λ(0) equal to a nonnegative value for all a, and choose 0 < ε 1 using the stop criterion.

2. The sub-problem for each program is locally solved by com-puting (38), and the solution, Ni,p∗

e (λ(t)), is then fed back to the master problem.

3. The BS updates its Lagrange multiplier with the sub-gradient iteration (41) and then feeds back the new Lagrange multiplier λ(t + 1) to its sub-problems.

4. Set t ← t + 1 and go to 2) (until the termination criterion is satisfied). �

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 107

Fig. 7. Utility for the base layer of (a) all programs and (b) each program as a function of distance.

Fig. 8. Utility for the enhancement layer of (a) all programs and (b) each program as a function of distance.

5. Simulation results

5.1. Simulation parameters

The simulation parameters are described in Table 2. The visual weights are calculated as described in [15]. For a cut-off frequency of 0.10, the visual weights of the base and enhancement layers are found to be 0.892 and 0.108, respectively.

The BSs are assumed to be uniformly distributed with users randomly distributed within each cell. The CoMP region consists of three adjacent sectors as shown in Fig. 1(a). Each layer is modulated using Quadrature Phase Shift Keying (QPSK). In or-der to satisfy the power constraint, the transmission power is E[|si

n,v |2] = (|C| − 2P i)/(|C| − 2P i + Njt) ≤ 1.0 for joint transmis-sion and E[|si

n,v |2] = 1.0 otherwise, where Njt represents the num-ber of programs that are jointly transmitted. The noise power is

Table 2Simulation parameters.

The number of adjacent cells (Noc) 6 (up to the 1st tier)The number of transmitter antennas (NT ) 2The number of receiver antennas (NR ) 2The path loss exponent (β) 4The number of carriers (Nsc) 512The number of subbands (|C|) 16The total bandwidth (BWT ) 0.5 MHzThe total number of layers (L) 2The modulation order (m) 2 (QPSK)

E[N0] = 0.1, and the threshold value for the joint transmission de-cision is rth = 2 [bit/Hz/sec] in order to stably deliver the visual data using QPSK. Additionally, the user number within a sector is K = ∑P i

p=1 |Ki,p| = 20 and the user number for each program is

108 T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115

assumed to be asymmetric: |Ki,1| = 8, |Ki,2| = 6, |Ki,3| = 4 and |Ki,4| = 2.

In the simulation, each layer is encoded at a target rate of 128 kbps. In order to compare performance, two non-coordinated (NC) resource allocation schemes are used, which separately assign resources to each BS.

• NC-1: Resources are equally assigned to each program layer. The number of sub-bands for each layer is |Cb | = |Ce| = 8.

• NC-2: Additional resources are assigned to the base layer, as the same size of CSA and the residues are assigned to the en-hancement layer. The resources are separately allocated to the base and enhancement layers with |Cb | = 12, |Ce| = 4.

• CSA: Using BS cooperation, a non-overlapped resource is as-signed to the base layer in order to avoid ICI. The resource allocation for the base layer consists of Step 1 and Step 2 in

Fig. 9. Utility gain for the base and enhancement layers between NC-2 and CSA/CSA-JT.

Section 4.1. The residues are then adaptively allocated to the enhancement layer (see Section 4.2): |Cb| = 12, |Ce| = 4.

• CSA-JT: This proposed scheme assigns a non-overlapped re-source to each program via the procedures described in Steps 1 through 4 in Section 4.1. The home BS then decides whether the program is jointly transmitted over the CoMP re-gion. In addition, each user determines which receive weight vector will offer the highest channel gain. The assignment for the enhancement layer is the same as in CSA, as follows: |Cb| = 12, |Ce| = 4.

5.2. Comparison of utility and outage probability

Fig. 7 shows the utility of the base layer delivered from the home BS as a function of distance. Within the cell border region of NC-1 and NC-2, the utility rapidly falls as ICI increases. Even if the base layer of the NCs are transmitted with loading ratios of 0.50 and 0.33, the decrease in the ICI is insufficient to main-tain the utility of the base layer. However, CSA and CSA-JT avoid ICI effectively and enhance the utility significantly via BS coop-eration. In particular, the CSA-JT at the cell border can provide a better enhanced utility than CSA due to the joint transmission. As shown in Fig. 7(b), we measure the utility according to each program, and the utility is enhanced even if the user number is small.

Fig. 8 shows the utility for the enhancement layer. The utili-ties of NC-2, CSA and CSA-JT for the enhancement layer are lower than that of NC-1 due to a relatively narrow bandwidth. However, the utilities of CSA and CSA-JT may be higher than that of NC-2 at the cell border. The utility of CSA-JT is slightly lower than that of CSA since the ICI is increased from other CoMP regions due to the joint transmission of the base layers. The proposed scheme for the enhancement layer can adaptively assign sub-bands to each pro-gram via the user distribution-based optimization in Section 4.2. Therefore, the enhancement layer of each program can be de-livered to the cell border with smaller utility loss, as shown in Fig. 8(b).

Fig. 9 shows the utility gain for the base and enhancement lay-ers between NC-2 and CSA/CSA-JT. The utility gains improve by 7% and 60% (CSA) and by 22% and 42% (CSA-JT) for the base and en-hancement layers, respectively, at the cell border relative to NC-2.

Fig. 10. Outage probability of the base layer of the (a) average of all programs and (b) each program as a function of distance.

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 109

Fig. 11. Comparison of performance in terms of the number of antennas for transmitter Nt and receiver Nr . Utility for the (a) base and (b) enhancement layers according to distance. The lines of dotted, solid and dashed indicate the number of antennas of 1, 2 and 4, respectively. In addition, the markers of downward- and upward-pointing triangles, square and diamond indicate the resource allocation schemes of NC-1, NC-2, CSA and CSA-JT, respectively. (c) Outage probability of the proposed schemes such as CSA and CSA-JT according to distance.

As a result, for inferior channel users located at the cell border, the utilities can be improved by the proposed schemes.

Fig. 10 shows the outage probability for the base layer as a function of distance. The outage probability of NC-1 is higher than others due to a higher loading ratio of 0.50. However, the pro-posed schemes for the base layer exhibit a relatively lower outage probability since the ICI can be avoided via BS cooperation. Ad-ditionally, CSA-JT can maintain a lower outage probability at the cell border, so that a higher QoS is experienced by users. The out-age probability in the simulation (the bold lines) is obtained by measuring the ratio of the outage occurrences through 100,000 empirical repetitions. In Fig. 10(a), the dotted lines represent the outage probability obtained from the numerical results via (20)and (30). As shown in Fig. 10(a), the simulation results agree with the numerical results. From the results, we could verify that these two results are correct by comparing each other. Moreover, via the numerical analysis including (20) and (30), we can find the op-timal parameters depending on various service condition without repeated attempts. In addition, Fig. 10(b) shows that the outage

probability of each program has a tendency similar to the overall numerical result.

5.3. Performance analysis according to parameters

In this section, we conduct additional experiments while vary-ing the simulation parameters including the number of anten-nas for the transmitter and the receiver, and the modulation or-ders. Then, we observe and analyze the behavior of the utility and their outage probabilities according to the distance from the home BS. In Figs. 11(a) and (b), when the visual signal are trans-mitted with larger NT and NR , the end users are can receive more utility. However, in the cell border region, the utility with larger NT and NR is reduced much rapidly. Fig. 11(c) shows the out-age probability for the base layer of CSA and CSA-JT. With larger NT and NR , the outage probability is higher than smaller NT

and NR . In conclusion, there are the trade-off relation between the amount of visual information delivered and the QoS depend-

110 T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115

Fig. 12. Comparison of performance according to the modulation order m. Utility for (a) base and (b) enhancement layers in terms of distance. The lines of dotted, solid, dashed and dash-dot indicate the modulation orders of BPSK, QPSK, 16-QAM and 64-QAM, respectively. In addition, the markers of downward- and upward-pointing triangles, square and diamond indicate the resource allocation schemes of NC-1, NC-2, CSA and CSA-JT, respectively. (c) Outage probability of the proposed schemes such as CSA and CSA-JT according to distance.

Fig. 13. Comparison of performance according to the number of allocated carriers for the base layer Ni,pb . Utility for (a) base and (b) enhancement layers in terms of distance.

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 111

ing on the number of antennas for the transmitter and the re-ceiver.

Fig. 12 shows the utility and the outage probability according to the modulation order m. As m is increased, more utility can be delivered near at the home BS while the utility rapidly falls at the cell border due to the ICI as shown in Figs. 12(a) and (b). In addition, Fig. 12(c) shows the outage probability for the base layer of CSA and CSA-JT as a function of distance. The outage probability becomes higher as m increased, so that the QoS of edge user can become lower. Thus, it is necessary to utilize a proper modulation factor depending on the service environment.

Figs. 13–15 show the utility while varying the simulation pa-rameters such as the number of allocated carriers for the base layer Ni,p

b , the number of layers L and the number of sub-bands for the channel feedback |C|. To compare the performance of CSA and CSA-JT with the state-of-the-art, we also evaluate the perfor-mance of the multi-resolution multicast (MRM) system [9].

By these results, we can observe several trade-offs in the pro-posed scheme. The first one is a trade-off of the QoS over the inner and outer cells as shown in Fig. 13. As more carriers are allocated

Fig. 14. Comparison of the utility according to the number of layers L.

for the base layer, the utility for the base layer is increased at the cell border. On the other hand, the coverage of delivering the en-hancement layer is conversely reduced due to the lack of residual carriers. In particular, CSA and CSA-JT at the cell border show bet-ter performance than MRM [9].

The second one is a trade-off between the link adaptation and the coding efficiency. When the spatial scalability is utilized by constructing the bitstreams with more layers, it is possible for the BS to transmit the video data more flexibly depending on the chan-nel condition. However, the coding efficiency and visual quality of SVC becomes worse due to the increase of the coding overhead. As shown in Fig. 14, in the inner region, AVC shows better perfor-mance than the other cases using SVC because the video data can be encoded more efficiently without the redundancy between the video layers. However, in the outer region, SVC can deliver higher utility than AVC since SVC can transmit the video data adaptively using multiple bitstreams depending on the channel condition. In addition, MRM [9] was designed to transmit only two layers, and the utility of MRM in the inner region is close to that of SVC with L = 2. However, at the cell border, the utility of MRM is reduced rapidly due to the ICI.

The last trade-off exists between the accuracy and overhead of feedback. Fig. 15 shows the utility according to the number of sub-bands for feedback. In the simulation, as the feedback |C| is increased, each sub-band includes smaller carriers and the feed-back of channel information is increased. However, the BS can also assign the sub-bands more efficiently to achieve higher QoS, while the system load needed for the feedback also increases. As the re-sult, the utility for the base layer is increased slightly. However, the number of sub-bands for the enhancement layers is reduced so that the utility for the enhancement layers is decreased as shown in Fig. 15(b). However, MRM [9] shows lower performance for the base and enhancement layers than CSA and CSA-JT.

However, far from the cooperation with multiple BSs over a multi-cell environment, most of previous works on video multi-cast have focused on improving the QoS using the radio resource allocation and the cooperation with adjacent transmitters within a single-cell environment. Here, we investigated an effective way to overcome the performance degradation at the cell edge by utiliz-ing the three trade-offs, but it still remains an open problem as further work.

Fig. 15. Comparison of performance according to the number of sub-bands for the channel feedback |C|. Utility for (a) base and (b) enhancement layers in terms of distance.

112 T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115

Fig. 16. The 16th reconstructed frame of the “Silent” sequence at a distance of 1.0 (a) NC-1, (b) NC-2, (c) CSA and (d) CSA-JT.

Fig. 17. Quality evaluation for the “Silent” sequence at a distance of 1.0 (a) PSNR and (b) SSIM.

5.4. Comparison of visual quality

Figs. 16–19 show the reconstructed visual quality at the cell border (di,k = 1.0).1 In this simulation, the original “Silent” and “Football” sequences in the CIF format (352 × 288) at30 frames/sec. The H.264/SVC (JSVM version 9.15 [40]) is used for video compression. The video sequence consists of one intra (I-) and consecutive inter (P-) frames, and the I-frame is repeated every 30 frames. In order to prevent error propagation, a passive

1 Test stimuli can be downloaded in [41].

error concealment method is applied based on previous error con-cealment experiments [12,15,42,43]. The error concealment adopts two strategies of the spatial and temporal error concealments. The missed macroblocks (MBs) in the I-frame are concealed by the spa-tial interpolation, while the information about missed MBs in the P-frame is drawn from previous frames in the decoding buffer.

In terms of the peak signal to noise ratio (PSNR) and the struc-tural similarity (SSIM) index [44], NC-1 and NC-2 demonstrate severely distorted visual quality, lower PSNRs and SSIMs when compared to other schemes, since the base layer is severely dis-torted due to the high ICI. The PSNR gains between NC-2 and CSA/CSA-JT increase to 7.97 dB and 12.48 dB (for “Silent”) and

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 113

Fig. 18. The 18th reconstructed frame of the “Football” sequence at a distance of 1.0 (a) NC-1, (b) NC-2, (c) CSA and (d) CSA-JT.

Fig. 19. Quality evaluation for the “Football” sequence at a distance of 1.0 (a) PSNR and (b) SSIM.

to 5.10 dB and 14.23 dB (for “Football”). The SSIM gains between NC-2 and CSA/CSA-JT increase to 0.1287 and 0.1599 (for “Silent”) and to 0.1888 and 0.2707 (for “Football”), respectively. CSA-JT is able to provide an improvement in visual quality (up to 8.25 dB in PSNR and to 0.0819 in SSIM) when compared to CSA due to the capacity gain afforded by the joint transmission.

5.5. Comparison of visual quality for the SHVC application

Recently, high efficiency video coding (HEVC) has been in-troduced which allows to deliver video data with higher visual

quality compared to H.264/AVC [45,46]. The scalable extension of H.265/HEVC (SHM version 2.0 [47]) is used for scalable video com-pression. The error concealment is applied based on the previous error concealment experiments [12,15,42,43,48]. Fig. 20 and Ta-ble 3 show the reconstructed visual quality at the cell border (di,k = 1.0) according to the video compression methods such as H.264/SVC and H.265/SHVC. In terms of PSNR and SSIM, the SHVC demonstrates higher visual quality than the SVC since the bit-streams can be delivered with less channel errors. In particular, the SHVC has higher performance (up to 2.40 dB for NC-1 and 1.14 dB

114 T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115

Fig. 20. Quality evaluation at a distance of 1.0. (a) PSNR and (b) SSIM for the “Silent” sequence. (c) PSNR and (d) SSIM for the “Football” sequence.

Table 3Performance comparison of video compression methods in terms of PSNR and SSIM.

Scheme Codec Silent Football

PSNR (dB) SSIM PSNR (dB) SSIM

NC-1 SVC 16.04 0.66 15.99 0.57SHVC 18.23 0.73 18.39 0.66

CSA-JT SVC 31.56 0.89 32.23 0.88SHVC 32.70 0.90 33.18 0.90

for CSA-JT in PSNR and up to 0.10 for NC-1 and 0.02 for CSA-JT in SSIM) when compared to the SVC.

6. Conclusions

Recently, the development of wireless communication tech-niques such as WiMAX and 3GPP-LTE has led to an increaseddemand for wireless video and multimedia services. Broadcast/multicast services represent a major application provided over cel-lular networks. However, similar to commercial cellular commu-nications, ensuring that the rate of data throughput is sufficient to satisfy the QoS of programs is important. We have presented a resource allocation technique utilizing the loading ratio control

of carriers in order to guarantee the QoS of multimedia multi-cast services over cooperative MIMO–OFDM networks. The ICI from adjacent cells is effectively mitigated by avoiding carrier collision. Using BS cooperation, the minimum QoS of the program with the user farthest from the home BS is guaranteed. Additionally, the QoS of the cell border user can be significantly improved by using joint transmission. Moreover, those users having superior channel conditions can receive programs with better visual quality via the enhancement layer of SVC while still satisfying the fairness crite-rion. In order to achieve more flexible adaptation to rapid channel variations, CSA and CSA-JT are conducted using channel feedback from the users. Finally, simulation results prove that the method described here is very effective within commercial cellular net-working environments, particularly when the user number is small in macro/micro cells.

References

[1] A. Ghosh, D. Wolter, J. Andrews, R. Chen, Broadband wireless access with WiMAX/802.16:current performance benchmarks and future potential, IEEE Commun. Mag. 43 (2005) 129–136.

[2] E. Dahlman, Y. Jou, Further evolution of 3G radio access, IEEE Commun. Mag. 44 (2006) 34–35.

T. Oh, S. Lee / Digital Signal Processing 33 (2014) 98–115 115

[3] 3GPP-TS-36.942, Radio frequency (RF) system scenarios (release 9) (2010).[4] B. Li, Y. Qin, C.P. Low, C.L. Gwee, A survey on mobile WiMAX, IEEE Commun.

Mag. (2007) 70–75.[5] 3GPP-TS-23.246, MBMS architecture and functional description (Release 6)

(2004).[6] A. Boni, E. Launay, T. Mienville, P. Stuckmann, Multimedia broadcast multicast

service – technology overview and service aspects, in: 3G Mobile Commun. Tech. 2004, 2004, pp. 634–638.

[7] A. Pande, V. Ahuja, R. Sivarag, E. Baik, P. Mohapatra, Video delivery challenges and opportunities in 4G networks, IEEE Multimed. 20 (2013) 88–94.

[8] S. Tarnoi, W. Kumwilaisak, Y. Ji, C. Kuo, Robust scalable video multi-cast with multiple sources and inter-source network decoding in lossy networks, J. Vis. Commun. Image Represent. 24 (2013) 602–614.

[9] A. Correia, J. Silva, N. Souto, L. Silva, A. Boal, A. Soares, Multi-resolution broad-cast/multicast systems for MBMS, IEEE Trans. Broadcast. 53 (2007) 224–234.

[10] Y. Wang, L. Chau, K. Yap, Bit-rate allocation for broadcasting of scalable video over wireless networks, IEEE Trans. Broadcast. 56 (2010) 288–295.

[11] A. Al Muhit, Error-resilient transmission of quality-scalable images over wire-less channels, Digit. Signal Process. 18 (2008) 588–597.

[12] T. Oh, H. Lee, S. Lee, Dynamic bandwidth and carrier allocation for video broad-cast/multicast over multi-cell environments, Wirel. Pers. Commun. 69 (2013) 1925–1945.

[13] J. Fan, X. Luo, Y. Zhang, Interference suppression with iterative channel and spatial covariance matrix estimation for LTE downlink, Digit. Signal Process. 24 (2014) 87–94.

[14] M. Sternad, T. Ottosson, A. Ahlen, A. Svensson, Attaining both coverage and high spectral efficiency with adaptive OFDM downlinks, in: IEEE VTC 2003 Fall, vol. 4, 2003, pp. 2486–2490.

[15] U. Jang, H. Lee, S. Lee, Optimal carrier loading control for the enhancement of visual quality over OFDMA cellular networks, IEEE Trans. Multimed. 10 (2008) 1181–1196.

[16] T. Oh, S. Lee, G.T. Gil, Increasing throughput and QoS using bandwidth and re-gion division with frequency overlay over multicell environments, IEICE Trans. Commun. E92-B (2009) 85–92.

[17] A. Al-nahari, F. El-Samie, M. Dessouky, Cooperative diversity schemes for uplink single-carrier FDMA systems, Digit. Signal Process. 21 (2011) 320–331.

[18] G. May, P. Ungere, A new approach for transmitting local content within digital single frequency broadcast networks, IEEE Trans. Broadcast. 53 (2007) 732–737.

[19] S. Wang, W. Liao, Cooperative multicasting for wireless scalable video trans-missions, IEEE Trans. Commun. 61 (2013) 3980–3989.

[20] J. Lee, B. Yeo, J. Kim, M. Jang, J. Choi, Energy efficient scalable video coding based cooperative multicast scheme with selective layer forwarding, IEEE Com-mun. Lett. 17 (2013) 1116–1119.

[21] S. Kim, H. Son, S. Lee, Receiver design for MIMO relay stations in multi-cell downlink system, IEEE Trans. Wirel. Commun. 11 (2012) 2446–2456.

[22] D. Gesbert, S. Hanly, H. Huang, S. Shitz, O. Simeone, W. Yu, Multi-cell MIMO cooperative networks: a new look at interference, IEEE J. Sel. Areas Commun. 28 (2010) 1380–1407.

[23] H. Zhang, X. Xu, J. Li, X. Tao, Subcarrier resource optimization for cooperated multipoint transmission, Int. J. Distrib. Sens. Netw. 2010 (2010), http://dx.doi.org/10.1155/2010/509297.

[24] LG Electronics, Network MIMO in LTE-advanced, R1-082942, 3GPP TSG RAN WG1 Meeting #54, 2008.

[25] S. Lu, Y. Chang, W. Chung, C. Chen, Realizing high-accuracy transmission in high-rate data broadcasting networks with heterogeneous users via coopera-tive communication, Digit. Signal Process. 25 (2014) 93–103, http://dx.doi.org/10.1016/j.dsp.2013.10.024.

[26] H. Zhang, H. Dai, Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks, EURASIP J. Wirel. Commun. Netw. 2004 (2004) 222–235, http://dx.doi.org/10.1155/S1687147204406148.

[27] Samsung Electronics, Inter-cell interference mitigation through limited coordi-nation, R1-082886, 3GPP TSG RAN WG1 Meeting #54, 2008.

[28] F. Kelly, A. Malullo, D. Tan, Rate control in communications networks: shadow prices, proportional fairness and stability, J. Oper. Res. Soc. 49 (1998) 237–252.

[29] T. Oh, S. Lee, Implementation of multimode–multilevel block truncation coding for LCD overdrive, IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E95-A (2012) 867–875.

[30] S. Winker, Issues in vision modeling for perceptual video quality assessment, Signal Process. 78 (1999) 231–252.

[31] S. Lee, M.S. Pattichis, A.C. Bovik, Foveated video compression with optimal rate control, IEEE Trans. Image Process. 10 (2001) 977–992.

[32] Z. Wang, A. Bovik, Embedded foveation image coding, IEEE Trans. Image Pro-cess. 10 (2001) 1397–1410.

[33] S. Lee, M.S. Pattichis, A.C. Bovik, Foveated video quality assessment, IEEE Trans. Multimed. 4 (2002) 129–132.

[34] H. Schwarz, D. Marpe, T. Wiegand, Overview of the scalable video coding ex-tension of the H.264/AVC standard, IEEE Trans. Circuits Syst. Video Technol. 17 (2007) 1103–1120.

[35] H. Ha, J. Park, S. Lee, A. Bovik, Perceptually unequal packet loss protection by weighting saliency and error propagation, IEEE Trans. Circuits Syst. Video Tech-nol. 20 (2010) 1187–1199.

[36] D. Gore, R. Heath, A. Paulray, Transmit selection in spatial multiplexing sys-tems, IEEE Commun. Lett. 6 (2002) 491–493.

[37] J. Arnbak, W. van Blitterswijk, Capacity of slotted aloha in Rayleigh-fading channels, IEEE J. Sel. Areas Commun. 5 (1987) 261–269.

[38] S. Boyd, L. Vanderberghe, Convex Optimization, Cambridge, 2004.[39] D.P. Palomar, M. Chiang, A tutorial on decomposition methods for networks

utility maximization, IEEE J. Sel. Areas Commun. 24 (2006) 1439–1451.[40] Joint scalable video model – reference software, http://ip.hhi.de/imagecom_GI/

savce/downloads/SVC-Reference-Software.htm, 2009.[41] Test stimuli for comparison of visual quality, http://wireless.yonsei.ac.kr/xe/

index.php?mid=page_ggZR78, 2014.[42] K. Lee, S. Lee, S. Lee, Optimization of delay-constrained video transmission for

ad hoc surveillance, IEEE Trans. Veh. Technol. 63 (2013).[43] E. Ryu, N. Jayant, Home gateway for three-screen TV using H.264 SVC and rap-

tor FEC, IEEE Trans. Consum. Electron. 57 (2011) 1652–1660.[44] Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli, Image quality assessment: from

error visibility to structural similarity, IEEE Trans. Image Process. 13 (2004) 600–612.

[45] ITU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11, High Efficiency Video Coding (HEVC) text specification draft 10, JCTVC document, L1003, Geneva, CH, Jan. 2013.

[46] G. Sullivan, J. Ohm, W. Han, T. Wiegand, Overview of the High Efficiency Video Coding (HEVC) standard, IEEE Trans. Circuits Syst. Video Technol. 22 (2012) 1649–1668.

[47] SHVC reference software (SHM 2.0), http://hevc.hhi.fraunhofer.de/svn/svn_SHVCSoftware, 2013.

[48] J. Nightingale, Q. Wang, C. Grecos, Scalable HEVC (SHVC)-based video stream adaptation in wireless networks, in: IEEE Int. Sympo. Personal Indoor and Mo-bile Radio Commun., 2013, pp. 3573–3577.

Taegeun Oh was born in Korea in 1984. He received his B.S. degree in Electrical and Electronic Engineering from Yonsei University, Seoul, Korea in 2007, and he is pursuing the Ph.D. degree in Multi-dimensional Insight Laboratory from Yonsei University, Seoul, Korea. His research interests are in the areas of multimedia communications, image/video signal processing and quality assessments.

Sanghoon Lee received the B.S. in E.E. from Yonsei University in 1989 and the M.S. in E.E. from Korea Advanced Institute of Science and Tech-nology (KAIST) in 1991. From 1991 to 1996, he worked for Korea Telecom. He received his Ph.D. in E.E. from the University of Texas at Austin in 2000. From 1999 to 2002, he worked for Lucent Technologies on 3G wire-less and multimedia networks. In March 2003, he joined the faculty of the Department of Electrical and Electronics Engineering, Yonsei Univer-sity, Seoul, Korea, where he is a full professor. He has been an Associate Editor of the IEEE Trans. Image Processing (2010–) and an Editor of the Journal of Communications and Networks (JCN) (2009–), and the Chair of the IEEE P3333.1 Quality Assessment Working Group (2011–). He served as the Technical Committee of the IEEE IVMSP (2014–), the General Chair of the 2013 IEEE IVMSP workshop, and a guest editor of IEEE Trans. Im-age Processing 2013. He has received a 2012 special service award from IEEE Broadcast Technology Society and 2013 special service award from IEEE Signal Processing Society. His research interests include image/video quality assessments, medical image processing, cloud computing, wireless multimedia communications and wireless networks.