rate-distortion based real-time wireless video streaming

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0923-5965/$ - se

doi:10.1016/j.im

�CorrespondE-mail addre

(H. Seferoglu),

oercetin@saban

(Y. Altunbasak

Signal Processing: Image Communication 22 (2007) 529–542

www.elsevier.com/locate/image

Rate-distortion based real-time wireless video streaming

Hulya Seferoglua,�, Ozgur Gurbuza, Ozgur Ercetina, Yucel Altunbasakb

aFaculty of Engineering and Natural Sciences, Sabanci University, Istanbul, TurkeybSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Received 14 August 2006; received in revised form 28 April 2007; accepted 28 April 2007

Abstract

This paper presents wireless video streaming techniques that exploit the characteristics of video content, transmission

history, and physical layer channels to enable real-time efficient video streaming over wireless networks to a wireless client.

The key contribution of the proposed video streaming techniques is the use of rate-distortion based, but simplified, low

complexity packet scheduling as well as forward error correction (FEC) rate selection. To this end, we develop an

optimization framework that jointly schedules the packets and selects the FEC rates. The rate-distortion optimized packet

scheduling and FEC rate selection provides the optimum quality video on the receiver side albeit at a high computational

cost. By some intelligent approximations, rate distortion optimized packet scheduling and FEC rate selection technique is

transformed into two sub-optimal but low complexity video streaming techniques that can provide high video quality. We

perform extensive simulations to understand the performance of our proposed techniques under different scenarios.

Results show that, the proposed techniques improve video quality on the average by 4 dB. We conclude that significant

benefits to end-user experience can be obtained by using such video streaming methods.

r 2007 Elsevier B.V. All rights reserved.

Keywords: Channel coding; Error correction; Multimedia streaming; Packet scheduling; Rate-distortion optimization; Video coding;

Wireless networks

1. Introduction

Advances in digital video coding systems such asH.264/AVC and MPEG-4 [10,17] coupled with theevolution of new standards with wireless network-ing are driving considerable research into highquality real-time wireless video streaming techni-ques. The fact that real-time video streamingtechniques in many wireless networks are limited

e front matter r 2007 Elsevier B.V. All rights reserved

age.2007.04.002

ing author. Tel.: +90542 5909563.

sses: [email protected]

[email protected] (O. Gurbuz),

ciuniv.edu (O. Ercetin), [email protected]

).

only to wireless channel adaptation show the extentof difficulties and challenges that real-time videostreaming still poses.

There are several impediments facing high qualityreal-time video streaming over wireless networks.Firstly, video data are vulnerable to channel errorsdue to dependency among video frames, because anerror occurring in a frame may propagate to thesubsequent frames and cause significant distortion.Secondly, real-time video streaming is sensitive tovideo packet delays since a portion of the videostream received after its deadline cannot be com-bined with the previously received portion, hencebecomes obsolete and results in distortion in the

.

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dx.doi.org/10.1016/j.image.2007.04.002

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ARTICLE IN PRESSH. Seferoglu et al. / Signal Processing: Image Communication 22 (2007) 529–542530

video. Thirdly, limited capacity, time varying, andnoisy nature of wireless channels make successfultransmissions unpredictable. Unlike wire-line chan-nels, wireless channels experience random packetlosses due to fading, shadowing, and interference.The causes of random losses may change in time dueto the mobility of the user, number of users in thearea, etc. Thus, transmission and error correctiontechniques should be adaptive to the changes in thechannel.

In this paper, we present a cross-layer protocolfor wireless video streaming in order to address theabove challenges. The protocol has two main partsworking jointly; a packet scheduler, and forwarderror correction (FEC). The packet scheduler andFEC collectively constitute a video streamingtechnique which decides on the transmission ofvideo packets and their FEC rates for protectionagainst channel noise by taking into account thepacket deadlines, dependencies, past history of thetransmissions and the quality of the wirelesschannel. In particular, we propose video streamingtechniques that perform scheduling and FEC basedon the optimal video quality improvement. Rate-distortion (R-D) optimized packet scheduling andFEC is a video streaming technique which mini-mizes the end-to-end distortion subject to the totalrate function and it is an extended version of theR-D optimized packet scheduling technique ofChou et al. [4]. In R-D optimized packet schedulingand FEC technique, both distortion and ratefunctions depend on packet scheduling and FECrate selection, and automatic repeat request (ARQ)mechanism to provide robust and effective videostreaming. Hence, the technique optimizes the R-Dfunction based on a hybrid FEC/ARQ error controlscheme. A similar approach using hybrid FEC/ARQ is proposed in [3], where R-D optimizedincremental error correction and packet schedulingmethod is used. Incremental error correction hastwo drawbacks. Computational complexity in-creases when the number of dependent packetsincreases due to usage of incremental packets, andoverhead increases since each incremental packetintroduces packet overhead. R-D optimized packetscheduling and FEC eliminates these drawbacks.

There is a plethora of work on the hybrid use ofFEC/ARQ in wireless video streaming. The firstgroup of FEC/ARQ techniques; [23,14,21] deter-mine FEC rates by taking into account the proper-ties of different video layers, and employs hybrid-Ior hybrid-II ARQ schemes as proposed in [16,15].

A similar FEC/ARQ technique is proposed in [13],where packet length is adjusted dynamically byusing the information about the channel conditions.The second group of FEC/ARQ selection techni-ques in [6,19] use stop-and-wait ARQ, and FECrates are selected from an FEC lookup table.

In addition to the hybrid FEC/ARQ schemes,individual FEC, ARQ, and packet schedulingmethods are also proposed in the literature. Ingeneral, FEC techniques use layer information ofdata as in [15,2]. The other approaches for errorcorrection are to select FEC rates in a R-Doptimized manner as in [5,29]. In [18], ARQ isperformed optimally considering the priority andtime constraint of the video packets, while onlyperceptual and temporal importance is used forARQ in [1]. Packet scheduling is proposedwithout ARQ in [26,12,11]. Packet schedulingwith ARQ is proposed in a R-D optimized mannerin [4].

R-D optimized packet scheduling and FECtechnique is computationally intensive, so it appearsunsuitable for real-time video streaming. Therefore,we design low complexity video streaming techni-ques called R-D based packet scheduling and FECtechniques that take into account R-D optimiza-tion. By using intelligent approximations andrelaxations to the original problem, R-D basedtechniques provide performances comparable tothe computationally intensive R-D optimizedtechnique.

The first R-D based technique named as jointpacket scheduling and FEC simplifies the computa-tion of R-D optimized packet scheduling and FECby separating the end-to-end distortion functioninto a set of individual packet distortion functionsarising from packet transmission and FEC rateselection policy. This separation reduces computa-tional complexity by ignoring the dependencyamong the possible solutions, but improves videoquality significantly by taking into account depen-dencies among video packets. Therefore, consecu-tive optimization of individual packet distortionsresults an approximate solution to the R-D optimi-zation. Type-I hybrid ARQ scheme is also employedby this technique to improve its robustness. Asimilar work to R-D based packet scheduling andFEC is proposed in [22] where distortion optimizedpacket scheduling is reduced to simpler terms todecrease the computational complexity, but noerror correction mechanism is employed in theoptimization.

ARTICLE IN PRESSH. Seferoglu et al. / Signal Processing: Image Communication 22 (2007) 529–542 531

The second R-D based technique named aspacket scheduling with tabulated FEC separatesthe packet scheduling and FEC rate selection tofurther reduce the computational complexity. Pack-et scheduling is performed by minimizing theindividual packet distortions, while FEC rate isselected from a table which maps the channelcondition into a suitable FEC, and is created bylong-term simulation results of the R-D optimizedpacket scheduling and FEC technique.

In summary, the major contributions of thispaper are the formulation of R-D optimized packetscheduling and FEC technique for the optimal videoquality improvement, and the derivation of lowcomplexity R-D based packet scheduling and FECtechniques, namely; joint packet scheduling andFEC, and packet scheduling with tabulated FEC forreal-time, efficient video transmission.

The paper is organized as follows. The nextsection defines the preliminaries about video encod-ing, streaming, and wireless communication. Sec-tion 3 presents details of R-D optimized packetscheduling and FEC technique while Section 4proposes R-D based real-time packet schedulingand FEC video streaming techniques. In Section 5,we show the simulation results, and the last sectionconcludes the paper.

2. Preliminaries

In this work, we consider the wireless last hop ofa video streaming system, where a gateway trans-mits video packets to clients over a wireless channelas depicted in Fig. 1. At the video server, H.264/AVC encoding is employed for video compressionand packetization due to its high compression

TransmissionBuffer

Wireless Gateway

video

A

VideoServer

packets

video

packets

High Speed andError Free

Link

− PacketScheduling

Wireless VideoStreaming

− FEC RateSelection

video

Fig. 1. Wireless video s

efficiency and its suitability for the current videostreaming services [10,25]. Considering the trade-offbetween the video quality and the compression rate,H.264/AVC encoding improves compression effi-ciency by removing the spatial and temporalredundancy in video frames using intra- and inter-coding schemes. However, these coding schemesintroduce dependency in the encoded video packets.The dependency structure of IPPP. . . encoded videosequence is shown in Fig. 2 where I frames are calledthe intra-coded and P frames are called the inter-coded frames. All P frames in the IPPP. . . structuredepend on the I frame, while each P frame alsodepends on the P frames that appear prior to itself.Thus, an error occurring in a frame may propagateto the end of the IPPP... structure, and causesignificant quality degradation in the video.

During video coding, H.264 encoder divides videoframes into smaller units called slices. Slices are thesmallest decodable units at the client side, and theirsize is determined by the video streaming applica-tion. A frame may consist of one or more slicesdepending on the information it contains and itsencoding type, intra- or inter-coding as shown inFig. 2. The motion and texture information of aframe is distributed to all slices in general, so aframe can be decoded even when only a fewcorresponding slices are received correctly. Clearly,the quality of the frame is increased with theincreasing number of successfully received slices.Note that, the decodability of a P frame depends onthe successful reception of its slices as well as theslices of all previous frames (see Fig. 2). Due to thetime varying nature of wireless channels, it is usuallypreferred to have short size packet transmissions toavoid the variation of the channel during a packet’s

packets

CKs

video packets

WirelessChannel

ACKs

WirelessClient

treaming system.

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{2,1} {F,1}{2,N2} {F,NF}{1,N1}{1,n}{1,1}

I frame, f = 1 P frame, f = 2 P frame, f = F

{f,n} is a video packet with f; frame number, n; slice number

Fig. 2. Dependency structure of IPPP. . . encoded video.

H. Seferoglu et al. / Signal Processing: Image Communication 22 (2007) 529–542532

transmission. Therefore, we employ slices as videopackets in our video streaming applications, anddetermine their sizes according to the channelcoherence time.

A video packet with frame number f and slicenumber n is assigned a specifier ff ; ng, and has a sizeBf ;n bytes, a decoding deadline tDTSf ;n

seconds,packet importance DDf ;n, and frame importanceDDf . tDTSf ;n

is the delivery deadline by which thepacket ff ; ng must arrive at the client, or becomesobsolete. The frame importance DDf is the amountof the decrease in the distortion if the frame isreceived and decoded successfully [27]. The packetimportance DDf ;n is the relative contribution of thesuccessfully delivered packet to the quality of theframe it belongs to.

The wireless channel considered as a flat Rayleighfading channel modeled by a finite-state Markovchain (MC), where the fading is approximated as adiscrete time Markov process with time discretizedtypically to the channel coherence time [7]. The setof all possible fading gains (equivalently, SNR orbit-error-rate (BER) levels) is modeled as the statesof the MC, and the channel variations are modeledas the transitions between these states that occur ateach interval with certain probabilities.

A packet transmitted with FEC rate1 Z at state s iscorrupted in the forward channel with errorprobability:

�F ðs; ZÞ ¼ 1�XdBf ;nðZ�1Þ=2e

k¼0

Bf ;nZ

k

!ðdðsÞÞk

� ð1� dðsÞÞðBf ;nZ�kÞ, ð1Þ

where dðsÞ is the symbol error rate of the channel atstate s. The duration between the packet transmis-sion time, t, and packet reception time is calledforward-trip time (FTT), and it is statisticallycharacterized with the probability density function,

1We employ Reed Solomon ðn; kÞ codes for FEC, where n and

k show the number of symbols in the packet after and before the

encoding, respectively. Reed Solomon codes are well suited to the

applications over wireless fading channels due to their high

error correction capability (these codes can correct at most

t ¼ bðn� kÞ=2c errors [24]) and their usefulness for applications

in which errors occur in bursts [8].

pF. A received packet may not be decoded bytime tþ t either because it is late or corrupted.We can compute the probability of this eventas, PfFTT04tg ¼

R1t pFðtÞdtþ ð1�

R1t pFðtÞdtÞ�F

ðs; ZÞ.In our system, the wireless client notifies the

gateway when a packet is received correctly andbefore its deadline. When ACK packet is trans-mitted to the gateway over a backward channelwhich is at channel state s0 and has data rate Z0, it isexposed to random delay and corruption with pB

and �Bðs0; Z0Þ, respectively. The probability of losinga packet due to corruption on the forward orbackward channels is given by the probability�Rðs; s0; Z; Z0Þ ¼ 1� ð1� �Fðs; ZÞÞð1� �Bðs0; Z0ÞÞ. Con-sidering the two way packet loss probabilities due tocorruption and delays, we let PfRTT04tg ¼R1t pRðtÞdtþ ð1�

R1t pRðtÞdtÞ�Rðs; s0; Z; Z0Þ denote

the probability that the gateway does not receivean ACK packet by time tþ t.

3. R-D optimized packet scheduling and FEC

In this section, we develop an R-D optimizedpacket scheduling and FEC technique by extendingthe framework proposed by Chou et al. in [4]. Theproposed technique in this paper determines theoptimal transmission policy in terms of packetselection and the associated FEC rates for thecurrent and next transmission opportunities. Ex-tending the transmission policy to include morethan two opportunities complicates the optimiza-tion problem significantly, and it becomes intract-able to formulate and minimize the distortionfunction.

Suppose that there are L ¼PF

f¼1Nf candidatepackets for transmission in the transmit buffer ofthe gateway, where F is the number of frames andNf is the number of packets in frame f. Let cnðtÞ andcnðt0Þ denote the FEC rate used for transmitting

packet n at the current transmission time t, and atthe next possible transmission time t0, respectively.For each packet ff ; ng in the transmit buffer, wedefine a rate vector pf ;nðtÞ :¼ ½cnðtÞ; cnðt

0Þ�. Let cnðtÞ

and cnðt0Þ take values from a predefined

FEC rate set C ¼ fZ0; Z1; . . . ; ZMg,where cnðtÞ ¼ Z0


represents the case when the packet n is nottransmitted at time t. We define the aggregate ratevector, pðtÞ, as a vector of rate vectors;pðtÞ:¼fpf ;nðtÞ : 1ofoF ^ 1onoNf g.

Let DðpðtÞÞ be the aggregate distortion observedwith total rate vector pðtÞ for all packets. Distortionoccurs due to the loss or delay of the packets in thewireless channel. Increasing the FEC rate reducesthe number of lost packets, but increases the cost ofthe transmission and the delay of the packets. Therate vector pðtÞ is associated with a cost RðpðtÞÞ. Weobtain the optimal solution by minimizing thefollowing function:

JðpðtÞ; lÞ ¼ DðpðtÞÞ þ lRðpðtÞÞ. (2)

In accordance with the discussion in [4], we definethe distortion function under the rate vector pðtÞ asthe difference between the maximum distortion,D0ðtÞ, when none of the packets are receivedcorrectly or within their deadlines, and the totalimprovement provided by those packets deliveredcorrectly with rate vector pðtÞ. Formally, thedistortion is

DðpðtÞÞ ¼ D0ðtÞ �XF

f¼1

DDf

�Yf

i¼1

� 1�

PNin¼1DDi;n�ðpi;nðtÞÞPNi

n¼1DDi;n

!, ð3Þ

where �ðpi;nðtÞÞ is the failure probability of packet n

of frame i due to channel errors and delay under therate vector pi;nðtÞ. A frame f is said to be successfullydecoded if it is received correctly and on-time, andall frames in the video sequence that f depends onare successfully decoded. However, a frame mayalso be decoded with a lower quality even if some ofits packets are missing. Considering that the qualityof a decoded frame increases with the number of itscorrectly received packets, we calculate the normal-ized improvement function of a frame i as 1�PNi

n¼1DDi;n�ðpi;nðtÞÞ=PNi

n¼1DDi;n which is a weightedsum of the packet failure probabilities. The productof these terms expresses the normalized improve-ment function for frame f, because the quality of adecoded frame also depends on the quality of thepreviously decoded frames.

The failure probability, �ðpi;nðtÞÞ, of packet n offrame i is calculated considering the loss probabilityof the past and the future transmissions of thepacket. In case ff ; ng is transmitted at time t with arate cnðtÞ, but not transmitted at the next opportu-

nity t0, i.e., cnðt0Þ ¼ Z0, the failure probability of this

packet is given by

�ðpi;nðtÞÞ ¼Y

jpanðtÞ; cnðtj ÞaZ0

PfFTT04tDTSi;n

� tjjRTT04t� tjg, ð4Þ

where anðtÞ is the number of previous transmissionsof ff ; ng until time t. The probability term in (4)represents the probability that the packet is failed inthe jth transmission at tj, either because FTT of thepacket exceeded the deadline or the packet wascorrupted given that no ACK packet is receivedcorresponding to this transmission. The product ofthe failure probabilities of the present and theprevious transmissions give us the overall packetfailure probability.

In a similar fashion, we consider the case whenpacket ff ; ng is transmitted at time t with rate cnðtÞ,and it is also transmitted at the next transmissionopportunity, t0, i.e. cnðt

0ÞaZ0. Then �ðpi;nðtÞÞ isdefined as follows:

�ðpi;nðtÞÞ ¼Y

jpanðtÞ; cnðtj ÞaZ0

PfFTT04tDTSi;n � t0g

� PfFTT04tDTSi;n � tjjRTT04t� tjg.

ð5Þ

In this case, we say that the transmission of packetff ; ng fails when none of the transmissions until thecurrent time t is successfully delivered given that noACK is received by the packet deadline, and alsothe transmission of the same packet at time t0 fails.

Having defined all the quantities necessary tocalculate the distortion function given in (3), wenow formulate the total transmission cost functionRðpðtÞÞ. RðpðtÞÞ is the sum of the estimated size of allthe packets transmitted previously or that will betransmitted in the current and the next transmissionopportunities:

RðpðtÞÞ ¼XF

i¼1

XNi

n¼1

Bi;nrðpi;nðtÞÞ, (6)

where rðpi;nðtÞÞ is the expected code rate for thepacket fi; ng. rðpi;nðtÞÞ is defined as the sum of thecode rate of the current transmission, and the coderate of the next transmission given that no ACKpacket is received until time t0 corresponding to theprevious transmissions of the same packet, i.e.,rðpi;nðtÞÞ ¼ 1=cnðtÞ þ ð1=cnðt

0ÞÞð1� PACK;nðtÞÞ.PACK;nðtÞ is the probability of successfully receivingan ACK packet corresponding to any previous

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η0

η1

η1

ηM

ηM

η1

ηM

η0

η0

ACKno ACK

Final States

Nonfinal States

Initial States(Current Tr. Time, t)

Final StatesIterim States(Next Tr. Time, t )

Fig. 3. Markov decision process for one packet optimization.


transmissions occurring by time t before the nexttransmission instant t0, and is given by

PACK;nðtÞ ¼ 1�Y

jpanðtÞ;cnðtj ÞaZ0

PfRTT04t0 � tjjRTT0

4t� tjg. ð7Þ

The minimization of JðpðtÞÞ ¼ DðpðtÞÞ þ lRðpðtÞÞ inorder to find the optimal rate vector pðtÞ and l iscomputationally difficult due to the inter-depen-dency between the individual rate vectors, pf ;nðtÞ. Inorder to simplify the optimization procedure, weemploy a dynamic programming solution, iterativesensitivity adjustment (ISA) algorithm, in a similarfashion as in [4] to determine both pðtÞ and l. ISAalgorithm minimizes the objective functionJðp1;1ðtÞ; . . . ;pf ;nðtÞ; . . . ; pF ;NF

ðtÞÞ iteratively, so ineach step, only one variable, pf ;nðtÞ is minimizedwhile the others are kept constant, i.e., single packetoptimizations are performed iteratively. It is shownthat the optimal rate vector at iteration p is given as

pðpÞf ;nðtÞ ¼ arg minpf ;nðtÞ

Spf ;n�ðpf ;nðtÞÞ þ lBf ;nrðpf ;nðtÞÞ,

(8)

where Spf ;n ¼

PFf 0¼f DDf 0

Qf 0

i¼1;iaf ð1� ðPNi

n¼1DDi;n

�ðpi;nðtÞÞÞ=ðPNi

n¼1DDi;nÞÞ is the sensitivity of the

decoder to the loss of the packet ff ; ng. Theiterations of ISA algorithm end, when JðpðtÞÞ

converges to the same value in consecutive itera-tions. Therefore, the order of the complexity of theR-D optimized packet scheduling and FEC employ-ing the ISA algorithm depends on the computa-tional complexity of the single packet optimizationand the number of iterations.The order of complex-ity of single packet optimization in (8) is determinedby the number of policies in the current and nexttransmission instants. If we model the transmissionby Markov process as in Fig. 3, it is seen that thenumber of policies is on the order of M2 where M isthe number of possible FEC rates. The complexityof the entire optimization is given by OðUM2Þ whereU is the number of iterations that depends on thesecond order of the number of optimized packets[20]. Therefore, the order of complexity increaseswith the second order of both number of packetsand FEC rates, and thus this technique is notsuitable for real-time applications. In the nextsection, we propose low complexity, R-D basedpacket scheduling and FEC techniques that aresuitable for real-time applications.

4. R-D based real-time packet scheduling and FEC

R-D optimized packet scheduling and FECtechnique optimizes the packet and FEC rateselection with the aim of providing optimum qualityat the receiver side video. However, high computa-tional complexity of this technique makes itunsuitable for real-time video streaming applica-tions. Hence, in the following, we propose lowercomplexity R-D based video streaming techniques:joint packet scheduling and FEC, and packetscheduling with tabulated FEC for real time videostreaming.

4.1. Joint packet scheduling and FEC

In R-D based joint packet scheduling and FECtechnique, we approximate the minimizationof the aggregate distortion function in (3) consider-ing separate individual minimizations of framedistortion functions; Df ;kðtÞ. As such, Df ;kðtÞ quan-tifies the distortion associated with frame f whenrate vector pf ;kðtÞ ¼ ½ckðtÞ; ckðt

0Þ� is used for trans-mission of packet ff ; kg as well as considering thedependency structure in the video sequence, and


defined as

Df ;kðtÞ ¼ DDf � DDf

� 1�DDf ;k�ðpf ;kðtÞÞ þ

PNf

n¼1;nakDDf ;n�ðpf ;nðtÞÞPNfn¼1DDf ;n

0@

1A

�Yf�1i¼1

1�

PNin¼1DDi;n�ðpi;nðtÞÞPNi

n¼1DDi;n

!. ð9Þ

Note that the minimization of Df ;kðtÞ with respect tothe rate vector gives us whether a particular packetis transmitted or not, and if transmitted theassociated FEC rate. Once the optimal Df ;kðtÞ isdetermined for 8f ; k, we select the packet ff ; kg,hence cf ;kðtÞ, for which Df ;kðtÞ is the minimum.

Now assume that we also simplify the rate vector,and only consider the transmission of a packet inthe current transmission opportunity. Then, thedistortion function of each packet k in each frame f,Df ;kðtÞ, can be decoupled from other packets, sincethere can be only one packet transmission per eachtransmission opportunity. Therefore, for each pack-et ff ; kg, we can calculate Df ;kðtÞ such thatpi;nðtÞ ¼ Z0;8ðiaf ; nakÞ. The probability of failureof a packet with pi;nðtÞ ¼ Z0 only depends on thepast transmissions, and thus, appears as a constantin the minimization of Df ;kðtÞ. Therefore, we denotethe packet failure probability as �i;n:¼�ðpi;nðtÞÞ where�i;n is determined by considering the packet trans-mission history as follows:

�
If a packet is transmitted at time tj , but its meanround-trip time is not passed yet;�i;n ¼ PfFTT04tDTSi;n � tjg. � If a packet is not transmitted yet, or transmitted,
but no ACK is associated with it, or its deadlineis passed; �i;n ¼ 1.
� If a packet is transmitted, and the corresponding
ACK packet is received, �i;n ¼ 0.

Considering the above classification, the framedistortion function is simplified to be a linearfunction of �ðpf ;kðtÞÞ:

Df ;kðtÞ ¼ DDf ðxþ kDD0f ;k�ðpf ;kðtÞÞÞ, (10)

where

k ¼Yf�1i¼1

1�XNi

n¼1

DDi;n�i;n

XNi

n¼1

DDi;n

, !, ð11Þ

DD0f ;k ¼ DDf ;k

XNf

n¼1

DDf ;n;

,ð12Þ

x ¼ 1� kþ kXNf

n¼1;nak

DDf ;n�f ;n

XNf

n¼1

DDf ;n

,0@

1A.

ð13Þ

The packet failure probability �ðpf ;kðtÞÞ is calcu-lated by (4), since we ignore the next transmission.Furthermore, the joint packet scheduling and FECtechnique only considers those packets as eligiblefor transmission if they are not transmitted pre-viously, or if they are transmitted, but ACK packetis not received within a mean round-trip time. Withthese assumptions, the condition on round-triptimes in (4) becomes irrelevant. Thus, framedistortion function, (10) is reduced to

Df ;kðtÞ ¼ DDf ðxþ kDD0f ;kPfFTT04tDTSf ;k� tgÞ.

(14)

The goal of the joint packet scheduling and FECtechnique is to minimize the frame distortionfunction subject to a transmission cost for8ff ; kg. The transmission cost function, Rf ;kðtÞ isdefined as the amount of data bytes necessaryto transmit a packet ff ; kg with the selected FECrate:

Rf ;kðtÞ ¼ Bf ;kð1=ckðtÞÞ. (15)

Notice that the algorithm solves the optimization;c�kðtÞ ¼ arg minckðtÞ

fDf ;kðtÞ þ lRf ;kðtÞ for 8f ; kg. Theproposed technique is summarized in Table 1. Theorder of complexity of this algorithm is OðL0MÞ,where L0 is the number of eligible packets fortransmission, and M is the norm of the FEC rateset. Recalling that eligible packets are packets thatare not transmitted before, or those packets that aretransmitted but no ACK is received within around trip time (RTT). In the worst case, L0 willbe equal to L. Order of complexity of thistechnique is thus given by OðLMÞ. We canconclude that the joint packet schedulingand FEC technique is a low complexity techniquewith its complexity depending on first order of thenumber of FEC rates and eligible packets. In thefollowing, we propose another R-D based streamingtechnique with even lower complexity by separatingthe packet scheduling and the FEC rate selectiondecisions.

ARTICLE IN PRESS

Table 1

Packet and FEC rate selection algorithm of R-D based joint

packet scheduling and FEC

1. For each packet ff ; kg2. Optimize c�kðtÞ ¼ arg minck ðtÞ

fDf ;kðtÞ þ lRf ;kðtÞg, Eq. (14),

(15)

3. Label the packets that are not transmitted before, or

transmitted at least a round-trip time ago

4. Order eligible packets in an ascending order according to

fDf ;kðtÞ þ l�Rf ;kðtÞg

5. Choose the first packet ff �; k�g in the order, and transmit it

with FEC rate c�kðtÞ

6.In the gateway, discard the packets if they are ACKed or their

deadlines are missed

7. Wait for the next transmission opportunity

Table 2

Packet and FEC rate selection algorithm of R-D based packet

scheduling with tabulated FEC

1. For each packet ff ; kg2. Label the packets that are not transmitted before, or

transmitted at least a mean round-trip time ago

3. Order eligible packets in an ascending order according to

Df ;kðtÞ, Eq. (17)

4. Choose the first packet ff �; k�g5. Read FEC rate table

6. If there is an FEC rate entry corresponding to the current

channel state, pick that FEC rate

7. Else run R-D optimized algorithm, determine FEC rate,

update the table

8. Transmit the selected packet with the determined FEC rate

9. In the gateway, discard the packets if they are ACKed or their

deadlines are missed

10. Wait for the next transmission opportunity


4.2. Packet scheduling with tabulated FEC

R-D based packet scheduling with tabulated FECis another efficient video streaming technique inwhich the packet scheduling and FEC rate selectiondecisions are separated. In this technique, the sameprocedure of R-D based joint packet and FEC isused for the packet scheduling, while the FEC rate isselected from an FEC rate table which consists ofFEC rate—channel state mappings.

Let us consider the frame distortion functionDf ;kðtÞ, and the rate function Rf ;kðtÞ as defined in(14) and (15), respectively. The frame distortionfunction is re-written by considering the definitionof PfFTT04tDTSf ;k

� tg:

Df ;kðtÞ ¼ DDf xþ kDD0f ;k

Z 1tDTSf ;k

�t

pFðtÞdt

þ 1�

Z 1tDTSf ;k

�t

pFðtÞdt

!�Fðs; rÞ

!!.

ð16Þ

In this technique, we assume that a packet selectedfor transmission is protected with a FEC ratechosen from an FEC rate table. Therefore, forpacket scheduling, we assume that the selected FECrate protects the packet against channel errors, andwe ignore the packet corruption probability, i.e.,�Fðs; rÞ ¼ 0. With this assumption, the frame distor-tion function is given as

Df ;kðtÞ ¼ DDf xþ kDD0f ;k

Z 1tDTSf ;k

�t

pFðtÞdt

!.

(17)

The rate function given in (15) is also reduced toRf ;kðtÞ ¼ Bf ;k, since we do not incorporate the FEC

rate selection in the packet scheduling. Further-more, we also ignore the minimization of the ratefunction, since the video packet sizes are the sameon the average. As a result, we minimize the framedistortion function in (17) for 8 ff ; kg and select thepacket with the minimum Df ;kðtÞ for transmission.

In order to construct the FEC rate table, the long-term simulations of R-D optimized packet schedul-ing and FEC technique is performed for differentvideo sequences, channel noise levels, and delaysensitivities. It is observed that the most importantparameter for FEC rate selection is the channelstate. We also observe that the deviation of FECrate—channel state mappings is negligible. Takinginto account these observations, we propose thefollowing FEC table construction procedure: after apacket is selected by minimizing the frame distor-tion function, the FEC rate table is checked whetherit contains an FEC rate corresponding to thecurrent channel state. If there is an entry in thetable, it is used for FEC coding, otherwise R-Doptimized packet scheduling and FEC technique isemployed for both the packet and FEC rateselection. Then, the selected FEC rate is used toupdate the FEC rate table.

We summarize the packet and FEC rate selectionalgorithm of the packet scheduling with tabulatedFEC technique in Table 2. The algorithm calculatesthe frame distortion function Df ;kðtÞ for all eligiblepackets, chooses a packet having the minimumframe distortion function, and determines the FECrate from the FEC rate table. Therefore, the orderof complexity of this algorithm is given by thenumber of packets as OðLÞ. This order of complexity


is extremely low, and thus this technique is suitablefor real-time video streaming services.

5. Performance analysis

In this section, we present and evaluate the videoquality performance of R-D optimized and R-Dbased packet scheduling and FEC techniques in oursystem as explained in Section 2 by simulations.

We employ QCIF size video sequences; Foreman,Carphone, and Mother & Daughter in the simula-tions. These video sequences are encoded, decoded,and their video peak signal-to-noise ratio (PSNR)quality is assessed with JM 8.3 software of theH.264/AVC standard [9]. Each video sequence withdata rate 70 kbps and frame rate 10 frames/s isreplicated to generate 2min video sequences.The size of IPPP... structure is set to 10 frameswith one I frame and nine P frames. Each frameconsists of one or more video packets (slices) with asize of 250 bytes on the average. Each video packetselected for transmission is protected with anFEC rate selected from the following set, Cs ¼

f0; 1; 0:98; 0:94; 0:91; 0:83; 0:71; 0:625; 0:55; 0:5; 0:33gwith 0 being the decision of not sending the packet.

When transmitted by a gateway, a video packetexperiences random delay on the forward channel(FTT) consisting of propagation delay on thechannel, and the waiting time at the client sidebefore reaching the video decoder. The propagationdelay is a function of the packet size, FEC rate, andtransmission data rate which is assumed to be100 kbps. Each packet is checked whether it is

Table 3

Steady state, state transition probabilities and BER values of Markov

States State transition Steady state 3 dB

Pi;i�1 Pi;iþ1 Pi;i BERi

s1 – 0.99 1:34� 10�2 8:76� 10�1

s2 0.38 0.61 7:33� 10�2 4:05� 10�1

s3 0.49 0.50 1:76� 10�1 1:24� 10�1

s4 0.43 0.56 2:54� 10�1 2:55� 10�2

s5 0.62 0.37 2:24� 10�1 3:36� 10�3

s6 0.67 0.32 1:48� 10�1 2:73� 10�4

s7 0.27 0.72 7:33� 10�2 1:28� 10�5

s8 0.77 0.22 2:70� 10�2 3:29� 10�7

s9 0.82 0.17 7:60� 10�3 4:07� 10�9

s10 0.12 0.87 2:20� 10�3 2:01� 10�11

s11 0.93 0.06 4:00� 10�4 2:83� 10�14

s12 0.99 – 1:00� 10�4 4:68� 10�18

received correctly and before its deadline, and isconcatenated with the packets of the same streamuntil it is decoded successfully. The completion timeof the whole process is also random, so FTT isselected according to an exponential distribution inour work. Similarly, an ACK packet generated bythe client also experiences a random delay in thebackward channel. Hence, the RTT including thedelay both in the forward and backward channels ismodeled with the exponential distribution.

In our simulation framework, Rayleigh fadingwireless channel is modeled with a 12-state MCmodel [28]. The Markov model is characterized bythe BER of each state, as well as the steady state andstate transition probabilities. The steady state andthe state transition probabilities depend on speed ofthe mobile clients and not on the channel SNR. TheBER of each state used in simulations is given inTable 3. The duration of remaining in the same statedepends on the coherence time of the channel whichin turn depends on the user speed and the channelfrequency. In our model, we assume nomadic orpedestrian wireless clients with 3 km/h speed, and2GHz channel frequency, which result in anaverage coherence time of 21ms. This durationensures us that the channel remains static during apacket transmission. In our experiments, we con-sider different channel quality levels assigningaverage SNR levels from the set f3; 5; 7; 9; 11g dB.

Figs. 4 and 5 show the PSNR performance of ourproposed techniques for various channel SNR levelsand different video sequences assuming 150msdelay tolerance. In the figures, R-D optimized

chain channel model at different channel SNR levels

5 dB 7 dB 9dB 11 dB

BERi BERi BERi BERi

7:58� 10�1 6:72� 10�1 6:01� 10�1 5:59� 10�1

2:65� 10�1 1:67� 10�1 1:13� 10�1 8:77� 10�2

4:84� 10�2 1:64� 10�2 6:47� 10�3 3:48� 10�3

4:63� 10�3 6:16� 10�4 1:06� 10�4 3:23� 10�5

2:20� 10�4 8:00� 10�6 4:39� 10�7 6:09� 10�8

4:88� 10�6 3:25� 10�8 4:03� 10�10 2:03� 10�11

4:65� 10�8 3:65� 10�11 6:96� 10�14 1:01� 10�15

1:73� 10�10 9:42� 10�15 1:79� 10�18 6:06� 10�21

2:15� 10�13 4:34� 10�19 4:98� 10�24 3:19� 10�27

7:22� 10�17 2:33� 10�24 8:67� 10�31 9:21� 10�35

4:44� 10�21 6:64� 10�31 3:41� 10�39 6:38� 10�44

2:20� 10�26 1:26� 10�39 2:08� 10�50 4:35� 10�55


packet scheduling and FEC, R-D based joint packetscheduling and FEC, and R-D based packetscheduling and tabulated FEC techniques arerepresented with T1, T2, and T3, respectively. Inour simulations, the performance of our proposedtechniques is compared with dynamic FEC andARQ technique, which is used widely in practicaldata transmission systems, and with R-D optimizedscheduling without FEC proposed in [4]. Inparticular, the dynamic FEC and ARQ techniquewhich is represented as T4 transmits the packets inthe transmission buffer according to first-in first-outrule and uses an error correcting codes capable of

3 4 5 6 7 8 9 10 1118

20

22

24

26

28

30

Channel SNR (dB)

PS

NR

(dB

)

Carphone

T1T2T3T4

Fig. 5. PSNR quality of Carphone and Mother & Da

3 4 5 6 7 8 9 10 1116

18

20

22

24

26

28

30

Channel SNR (dB)

PS

NR

(dB

)

Foreman

T1T2T3T4T5

Fig. 4. PSNR quality of Foreman video sequence for different

SNR levels.

correcting 99% of the symbol errors. Since thewireless channel is a time varying channel, FECrates are dynamically assigned according to the stateof the channel at the time of packet transmission. Apacket is not transmitted again within a mean RTTof its transmission, but it is automatically trans-mitted again if no ACK is received in a mean RTT.The R-D optimized packet scheduling representedas T5 schedules packets according to R-D optimiza-tion, and only employs ARQ mechanism instead ofhybrid FEC/ARQ.

We note from Fig. 4 that proposed R-Doptimized and R-D based techniques in this paperoutperforms the R-D optimized packet schedulingfor all channel SNR levels. The significant qualityimprovement of the proposed techniques shows theimportance of FEC usage for video streaming overwireless channels. Even if the average channel SNRlevel is high, wireless channel has some noisy statesdue to fading. The proposed techniques are able tocorrect errors that occur during fading with FEC,however R-D optimized packet scheduling facespacket losses in these states, and no packet istransmitted successfully during fading. Therefore,R-D optimized packet scheduling without FECperforms poor for wireless channels. In light of thisobservation, we conclude that FEC usage isrequired for wireless channels, and henceforth wewill compare the proposed R-D optimized and R-Dbased techniques with dynamic FEC and ARQtechnique.

Figs. 4 and 5 show that the R-D optimized andR-D based techniques improve video quality better

3 4 5 6 7 8 9 10 1122

24

26

28

30

32

34

36

38

40

42

Channel SNR (dB)

PS

NR

(dB

)

Mother and Daughter

T1T2T3T4

ughter video sequences for different SNR levels.


than the dynamic FEC and ARQ for all channelSNR levels and video sequences. The reason for thesignificant quality improvement of the proposedtechniques is due to the protection of the crucialpackets by considering the importance and dead-lines of packets, the wireless channel conditions, andthe packet transmission history. However, dynamicFEC and ARQ technique does not have such amechanism, and treats all the packets the same. Thiscauses loss of important packets, and results insignificant distortion. An example of such a case isgiven in Fig. 6 at 7 dB channel with 150ms delaytolerance for Carphone video sequence. In thisexample, when the wireless channel fades, thequality of the video reduces for all algorithms, butthe degradation in the video quality with dynamicFEC and ARQ is much more since one or morecrucial packets are lost due to fading, while they areprotected by R-D optimized and R-D basedtechniques. Note that, the proposed techniquesmay also fail to transmit all of the video packetsdue to the limited channel data rate and theincreasing packet sizes during channel fades, butthese failed packets are chosen from less importantpackets that degrade the video quality much lessthan the other packets. Therefore, R-D optimizedand R-D based techniques encounter qualitydegradation that is much lesser than that of thedynamic FEC and ARQ technique as it canbeperceptually observed in Fig. 6.

As seen in Figs. 4 and 5, the video quality of theproposed techniques compared to the dynamic FEC

0 10 20 30 40 50 6010

15

20

25

30

35

40

45

frame number

PS

NR

(dB

)

T1T2T3T4

Fig. 6. PSNR variation with frame number and correspond

and ARQ increases as the channels SNR increases.For example, the PSNR improvement is around1 dB in a 3 dB channel, while it increases up to 5 dBin a 11 dB channel. This is because, the transmissionopportunities are very rare in 3 dB channel due toharsh channel conditions. R-D optimized and R-Dbased techniques always try to send the mostimportant packet with the best FEC rate, butcannot achieve this in a 3 dB channel, because mostof the important packets are discarded, since thedeadlines of these packets pass before a transmis-sion opportunity occurs. However, when channelSNR levels approach to the common values, thepacket protection of R-D optimized and R-D basedtechniques improves and thus the PSNR improve-ment increases.

Furthermore, the PSNR improvement of R-Doptimized and R-D based techniques over dynamicFEC and ARQ is higher for the video sequenceMother & Daughter as it is seen in Fig. 7 for 150msdelay tolerance in a 5 dB channel. The reason of thisbetter performance is due to dependency structureof the different video sequences. The dependencystructure depends on the rate of the change of thevideo scenes in the video sequences. The rate ofchange is higher in Foreman and Carphone thanMother & Daughter. In slowly changing videos, Pframes contain small amount of data (for exampleone packet size) due to its similarity to theirprevious frames, while P frames are generallycomposed of more than one packet. As mentioned,a frame may be decoded although a few of its

70 T3

T1

T4

T2

ing encoded frames of the Carphone video sequence.


packets are received correctly. However, if a frameis composed of one packet, that packet should bereceived correctly in order to be able to decode itsframe. Therefore, we can say that the number ofimportant packets is higher in Mother & Daughter,and the higher quality improvement of the proposedtechniques is expected since these techniques favorthe dependency and importance of the packets.

Finally, if the PSNR improvement of the R-Doptimized and the R-D based techniques arecompared, it is seen that the R-D optimizedtechnique exceeds the R-D based techniques byapproximately 1 dB. The reason of this difference isthat the R-D based techniques are approximationsof the R-D optimized technique, and hence they donot include some of the solutions of the optimalsolution set. However, 1 dB is an acceptable PSNRdifference when we consider their significant quality

50 100 150 200 250 300

16

18

20

22

24

26

28

Delay Tolerance (ms)

PS

NR

(dB

)

Foreaman

T1T2T3T4

50 100 150

19

20

21

22

23

24

25

26

27

28

29

Delay Tole

PS

NR

(dB

)

Carp

T1T2T3T4

Fig. 8. PSNR quality of video sequenc

Foreman Carphone Mother and

Daughter

0

5

10

15

20

25

30

Video Sequences

PS

NR

(dB

)

T1

T2

T3

T4

Fig. 7. PSNR comparison for the proposed techniques with

respect to video sequences.

improvement which is 4 dB on the average, and lowcomputational complexity. Therefore, R-D basedtechniques approximate well to the R-D optimizedtechnique, and they are rather good solutions to thepractical video streaming applications.

Fig. 8 depicts the PSNR improvement of the R-Doptimized and R-D based techniques as well asdynamic FEC and ARQ in a 5 dB channel fordifferent delay tolerances and video sequences. Thequality improvement of the proposed techniqueover dynamic FEC and ARQ is not significant;almost negligible for Mother & Daughter videosequence, when delay tolerance is 50ms. If weconsider the average packet size, FEC rate, andchannel data rate in a 5 dB channel, it is seen thattransmission time of a packet is comparable withthe delay tolerance at low delay tolerances. There-fore, (re)transmissions of some packets becomeimpossible, and most of the important packetscannot be delivered to the client successfully, anddistortion increases. The effect of loosing importantpackets is more severe for Mother & Daughter videosequence, because most of its packets are importantpackets. However, the quality improvement in-creases when delay tolerance is set to more reason-able values. Moreover, the proposed techniquesachieve almost the highest quality improvementwhen delay tolerance increases although the channelSNR level is 5 dB, which is a noisy channel.

The simulation results for different channel SNRlevels, video sequences, and delay tolerances showthe effectiveness of the proposed R-D optimizedand R-D based techniques for video streaming overdynamic FEC and ARQ technique. Furthermore,the computational complexities are given asOðUM2Þ, OðLMÞ, and OðLÞ for R-D optimizedpacket scheduling and FEC, R-D based joint packetscheduling and FEC, and R-D based packet

200 250 300

rance (ms)

hone

50 100 150 200 250 300

24

26

28

30

32

34

36

38

Delay Tolerance (ms)

PS

NR

(dB

)

Mother and Daughter

T1T2T3T4

es for different delay tolerances.


scheduling and tabulated FEC, respectively. L is thenumber of packets at the gateway, M is the norm ofFEC rate set, and U is the number of iterations ofR-D optimization and it is directly proportionalwith L2. From these analytical results, it is seen thatR-D based packet scheduling and FEC techniquesare suitable for real-time applications. We canconclude from simulation results and analyticalcalculations that R-D based techniques provide real-time efficient video streaming over wireless channels.

6. Conclusions

We have presented R-D optimized and R-Dbased packet scheduling and FEC techniques forwireless video streaming by exploiting; (1) videocharacteristics, (2) transmission history, and (3)characteristics of the wireless channel to enableeffective, low complexity video streaming over awireless channel to a wireless client. By taking theadvantage of the unequal sensitivity of video qualityto the loss of different packets while making packetscheduling and FEC rate selection decisions, theproposed techniques exhibit superior performanceover existing solutions. The simulation results showthat R-D optimized and R-D based techniquesimprove the PSNR quality over dynamic FEC andARQ technique by up to 5 and 4 dB, respectively.The high computational complexity of R-D opti-mized technique makes it not applicable to real-timewireless video streaming services, while R-D basedtechniques can be used in real-time applications dueto their low computational complexity. Finally, thenovel contributions of this paper can be summar-ized as in the following.

�
Formulation of R-D optimized packet schedul-ing and FEC technique for optimal packetscheduling and FEC rate selection. � Introduction of R-D based packet scheduling
and FEC and packet scheduling with tabulatedFEC techniques for near optimal and lowcomplexity packet scheduling and FEC ratedecisions.

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