Adaptation engine for a streaming servicebased on MPEG-DASH

Laura Pozueco & Xabiel García Pañeda & Roberto García &

David Melendi & Sergio Cabrero & Gabriel Díaz Orueta

Received: 30 October 2013 /Revised: 26 February 2014 /Accepted: 14 April 2014# Springer Science+Business Media New York 2014

Abstract HTTP Video streaming has become a strong candidate for video transmission on theInternet thanks to the abundance of web infrastructure. With the recent standardization of thenew MPEG Dynamic Adaptive Streaming over HTTP (DASH), the flexibility and implanta-tion of adaptive video systems has increased due to the fact that DASH can operate on aconventional web infrastructure. In this paper we propose an estimation and adaptation systemfor DASH. The proposed adaptive algorithm is based on client buffer threshold and smooththroughput measures (based on the throughput of previous segments). Before DASH, thestandard of Scalable Video Coding (SVC) also arose from the idea of adaptation. Both systems(adaptive system based on SVC and the proposed system for DASH) are compared in terms ofVideo Quality (VQ) metrics. The results show that the proposed system reacts properly tochanges in the network capacity, while maintaining a minimum level of segments in the buffer.The user-perceived quality is better than in the SVC-based adaptive system although thegenerated traffic is higher.

Keywords Dynamic adaptive streaming over HTTP (DASH) . Scalable video coding (SVC) .

Adaptation algorithms . Bandwidth estimation

Multimed Tools ApplDOI 10.1007/s11042-014-2034-y

L. Pozueco (*) :X. G. Pañeda : R. García : D. Melendi : S. CabreroInformatics Department, University of Oviedo, Gijón, Spaine-mail: pozuecolaura.uo@uniovi.es

X. G. Pañedae-mail: xabiel@uniovi.es

R. Garcíae-mail: garciaroberto@uniovi.es

D. Melendie-mail: melendi@uniovi.es

S. Cabreroe-mail: cabrerosergio@uniovi.es

G. D. OruetaElectrical and Computing Department, Spanish University for Distance Education (U.N.E.D), Madrid, Spaine-mail: gdiaz@ieec.uned.es

1 Introduction

Audio/video services on the Internet are in constant evolution to meet the needs of perceivedquality by the client. However, guaranteeing a certain quality of experience is not exempt fromproblems: transmission errors, delays and bandwidth availability are factors that affect theperceived quality in a streaming video service. Therefore, adaptive streaming over IP networksis highly recommended, in order to guarantee a certain quality of experience under changingconditions in the available bandwidth.

In previous work we approached this problem from the point of view of content adaptationto available bandwidth, studying and proposing adaptation algorithms for two differentsolutions: firstly, live transcoding of content [9] and secondly, the use of scalable encoderssuch as SVC [20] to make the system more scalable than the previous one. Results of the SVC-based system show that the evaluated solution adapts accurately and rapidly to changes inavailable bandwidth, preventing unwanted oscillations in the rate adjustments. However, thissolution presents two main problems. The first is related to the overheads incurred by theserver, on which all adaptation logic resides. The other refers to the need for a specificinfrastructure for distribution, which involves high deployment costs.

Changing the paradigm of streaming audio and video over RTP, a new generation of HTTP-based streaming applications has emerged, based on a technology called Dynamic AdaptiveStreaming over HTTP (DASH) [11], recently standardized in late 2011. DASH allows videostreaming adaptively over the Internet using the HTTP protocol, which presents an advantagefor deployment and provides streaming services for users with dynamic network conditionsand heterogeneous devices. In DASH, the content is encoded in multiple versions withdifferent bitrates. Each of the versions is chunked into smaller video pieces, which are sentto the clients via standard HTTP servers. Unlike SVC-based systems, the adaptation logicresides on the client’s side, so problems related to scalability issues could be solved.

The adaptive streaming over HTTP is gradually being adopted by content and serviceproviders on the network, thanks to the advantages it offers in terms of user-perceived qualityand resource utilization. The use of HTTP/TCP connections allows reusing the existingnetwork infrastructure (such as caches) and the cancellation of the typical problems of RTP/UDP related to firewalls and NAT.

In this paper we propose a change of perspective in relation to previous work,addressing the design and implementation of an estimation and adaptation module forDASH technology. How the client makes the bandwidth estimation and decisions toadapt the contents are outside the scope of the DASH standard, so it presents an openissue. To cover the gap in the standard, we propose a solution in which the client willrequest the content from the server based on estimated throughput metrics and the rateoccupancy of the client’s buffer. An algorithm will be responsible for calculating theoptimal representation to request based on the value of the metrics. Unlike previoussystems [20], adaptive logic is fully implemented on the client’s side, thereby reduc-ing the load on the server. The proposed adaptation method does not require anyadditional feedback information between the client and the server.

To date, few studies have focused on the development of estimation and adaptationmodules for DASH, and none compare the results with an adaptive RTP streaming systembased on SVC. Both technologies arise from the need to adapt content to the availablebandwidth, and should be compared in terms of video quality metrics due to the differencesbetween them. Our adaptation solution for DASH is implemented and evaluated experimen-tally in a real environment. Furthermore, the proposed system is compared with an adaptivestreaming system based on SVC in terms of perceived quality.

Multimed Tools Appl

The main contributions of this work include, on the one hand, the design and implemen-tation of estimation and adaptation algorithms for an HTTP streaming service based on theDASH standard and, on the other hand, the video quality evaluation of the proposed systemversus an adaptive RTP streaming system based on scalable codecs. Unlike our study, otherworks use commercial clients in their evaluations or implement non-optimal adaptationsolutions. Moreover, our proposed algorithm has the advantage of simplicity to be integratedin a real environment without being intrusive. The results show that the proposed algorithmadapts the bitrate of the transmission to the end-to-end network capacity, with effective controlof the minimum levels of the client buffer. Furthermore, in most cases, the video quality isbetter than the video quality in an SVC adaptive system. However, HTTP/TCP imposes moreoverheads than RTP and the use of available bandwidth is better in RTP.

The rest of the paper is organized as follows: Section 2 summarizes the main works relatedto this study. Section 3 describes in detail the proposed system based on DASH technology. InSection 4 we analyze the behavior of the proposed system and in Section 5 we compare theresults obtained with a system based on SVC. Finally, conclusions and future work arediscussed in Section 6.

2 Related work

The purpose of an adaptive streaming system is to adjust the transmission bitrate of thecontents to the available bandwidth, taking into account variations in the network conditions.This avoids lost frames and frozen images during playback, with the consequent improvementof the perceived quality by the client.

There are many related works in the field of the adaptation of video streaming over UDP[12] [5]. In spite of the general trend to use UDP protocol for sending audio and video in realtime due to its low latency, HTTP streaming has achieved a lot of interest. Traditionally, TCPwas not considered a feasible protocol for sending audio/video. However, HTTP streaming hasseveral advantages that make it a popular technology in commercial deployments. Neverthe-less, some commercial solutions do not implement resource estimation and adaptation pro-cesses in the transmission of the contents. Authors in [10] conducted a study of the behavior ofdifferent commercial streaming servers, revealing that they are unable to estimate the availablebandwidth and therefore cannot serve videos with the best quality possible. Referring toadaptive commercial clients, Akhshabi et al. [2] provide an experimental evaluation identify-ing the inefficiencies of analyzed players. However, the different commercial options forHTTP streaming (Apple’s HTTP Live Streaming,1 Adobe’s Dynamic HTTP Streaming,2

and Microsoft’s Smooth Streaming3) use different segment formats and need proprietaryclients. The DASH standard emerged to solve this problem, enabling interoperability betweenservers and clients from different vendors.

In the new trend of DASH streaming, content adaptation is implemented by creatingmultiple alternatives (or versions) and moving the adaptation logic to the client, who decidesthe next segment to request. DASH standard does not cover the design of the adaptationprocess so there are different proposals focused on this aspect. A first approach in the study ofdifferent multimedia players under different evaluation conditions is presented in [1]. In theanalysis, the authors focused on the behavior of the players but not on the adaptive algorithms

1 http://tools.ietf.org/html/draft-pantos-http-live-streaming-112 http://www.adobe.com/es/products/hds-dynamic-streaming.html3 http://www.iis.net/downloads/microsoft/smooth-streaming

Multimed Tools Appl

or on the metrics to estimate the available bandwidth. Our work is focused on the design andimplementation of estimation and adaptation algorithms.

Authors in [24] perform the adaptation from metadata added to the DASH syntax andperform a throughput estimation based on previous segments. However, this proposal does nottake into account the use of mechanisms for controlling the client buffer occupancy levels. Forthat reason, if the available bandwidth decreases significantly, the user may experienceinterruptions in playback. Our proposed algorithm includes the buffer occupancy as a metricin the decision process to perform adaptation. In this sense, authors in [25] develop anadaptation algorithm for DASH with client-side buffered video time as feedback signal. Morerecently, Biernacki et Tutschku [7] evaluated the influence of network impairments and theirimpact on the video quality, concluding that the implementation of buffering strategies on theclient’s side helps to improve the user-perceived quality.

Liu et al. [15] present an adaptive algorithm for MPEG-DASH with step-wise increases andaggressive decreases for the bitrate adaptation. The primary metric used to detect variations inthe available bandwidth is the measure of the smoothed throughput. However, authors in [25]analyzed the implementation of the proposed solution by Liu et al. and concluded that theproposed algorithm is too conservative and the results are less sensitive to changes in networkconditions than other implementations due to the smoothed throughput measurement. Never-theless, our experience in adaptive streaming services [9], [20] leads us to adopt a conservativebehavior in order to avoid congestion situations. The proposed algorithm in [15] is imple-mented in the network simulator NS-2 and the evaluation results show significant fluctuationin the video quality. Our proposed architecture is implemented in a real environment and weuse techniques of network emulator to perform variations in the available bandwidth in acontrolled environment.

Without forgetting scalable codecs, some works propose the integration of SVC with HTTPtransmission. Famaey et al. [8] analyzed the behavior of two adaptive systems based onDASH, which differ in the encoding scheme used: the first employs AVC coding while thesecond employs SVC. The aim of this work is the evaluation and comparison between bothsystems under different conditions. They use a solution based on Microsoft Smooth Streamingsince the analysis is not focused on the adaptive algorithm, as the work that we propose.Schierdl et al. [22] show the benefits of using HTTP adaptive streaming in combination withSVC to overcome link interruptions in mobile networks. However, other authors [13] assessthe integration of DASHwith SVC streams and conclude that the technology is not feasible forstreams below 1 Mbps. Developing DASH solutions using SVC requires a specific analysis toexploit the potential of both solutions. For example, the study of the optimal quality selectionpolicies for the layers in SVC is analyzed in [4]. Regarding caching efficiency, authors in [21]discussed the advantages in terms of resource utilization in the usage of SVC with DASH andcompared the results with a non-adaptive service with AVC.

Our previous works in the field of adaptive streaming evolve from transcoding-basedsolutions [9] to SVC-based systems [20], always using RTP as the transport protocol. In thispaper, we focus on adaptation for DASH systems. The main contributions of this paper are thedesign, implementation and evaluation of an estimation and adaptation module for a DASHsystem, not included in the standard. Also, the proposed solution is compared, in terms ofobjective quality metrics with an adaptive system based on SVC.

The proposed solution is implemented using libdash libraries [19], as they are the officialreference software for the DASH standard, unlike other proposals which develop a clientprototype using GStreamer framework [17]. To estimate the available bandwidth, we use thethroughput of the previous segments and, as opposed to the system presented in [24], we alsoinclude the client buffer occupation to perform the adaptation process. Unlike the system in

Multimed Tools Appl

[15], our work employs NS-3 as network emulator and results, obtained under differentnetwork occupation conditions, show stable adaptations throughout the experiments. Theresults, in terms of video quality metrics, are compared with our previous adaptive systembased on SVC. In this sense, we have employed the main conclusions obtained in [3] to designour adaptation algorithm.

3 Description of the proposed system architecture

Our HTTP streaming service follows the architecture proposed in the DASH standard, with theadaptation logic implemented on the client’s side. As the DASH standard does not cover thedesign of the adaptation system, we propose the design and implementation of the estimationand segment selection algorithms to construct the adaptation system for the DASH clients.

The following describes the client–server system based on DASH and the proposedadaptive system.

Figure 1 summarizes an example of a scenario between an HTTP server and a DASH client,highlighting the designed modules. The multimedia content stored on the HTTP serverconsists of two distinct elements: Media Presentation Description (MPD) and the segments.The MPD is an XML file that describes the characteristics of the different versions of thecontent to be downloaded that are available on the server. DASH requires several represen-tations (versions) available on the server. Each representation is an option of encoding (such asthe coding rate or the spatial or temporal resolution). Multimedia content is divided intosegments. Each segment has its own URL and is self-contained so the player can request andreproduce it individually. However, the DASH standard does not cover the adaptation mech-anism that the client can use when deciding which segment to request. MPEG-DASHspecification refers only to the MPD and the segment formats. The MPD delivery, the mediaencoding and client behavior are outside the scope of the standard. Our architecture includesthe adaptation system on the client’s side to select the most appropriate segment to download,according to the estimated available bandwidth. For more information about the structure andbasic concepts of DASH, refer to [23].

Figure 2 describes the algorithms used in the adaptation process on the client’s side. Thesealgorithms correspond with the highlighted modules of Fig. 1. The estimation algorithmcalculates the available bandwidth. The segment selection algorithm selects the segmentaccording to the estimated available bandwidth and starts streaming, using HTTP-GETrequests. In our proposal, estimating the available bandwidth is performed based on the

Fig. 1 System description

Multimed Tools Appl

throughput of previous segments. We also included the occupancy rate of the client buffer as ametric to select the appropriate segment to download. The complete adaptation process iscarried out for each segment request.

In the estimation module, the client measures the time to download each segment toestimate the throughput, considering the average of the last 5 values (like our previousestimation and adaptation system for SVC presented in [20]) and adding a safeguard of20 %. This conservative behavior allows us to avoid unreliable situations such as fluctuationsin available bandwidth [27]. Furthermore, according to the work presented in [26], in TCPstreaming the available bandwidth should be at least twice the bitrate of the video. Otherauthors [7] conclude in their study that the conditions are less restrictive, being sufficient thatthe network throughput is 15 % higher than the video encoding rate. Our proposal is acompromise solution between the two, adding a safeguard of 20 %.

Buffer management is a basic function for adaptive streaming clients, as it prevents theunfavorable effects of congested networks in the playback of the content. In the segmentselection algorithm, the client takes into account the buffer occupancy level, expressed in termsof the percentage of the total buffer size, and the bitrate of the different representations of thecontent. All the necessary information about details of the content stored in the server(including the bitrate of different representations) is extracted from the MPD.

In the proposed segment selection algorithm, the client will request the lowestquality version of the contents until a minimum threshold is reached in the buffer,set to a value of 35 % occupancy. This allows to partially fill the buffer in a quickmanner and prevents buffer starvation in congested scenarios. Some authors focus onadaptation techniques based on buffer levels as a primary metric. The work presented in[28] is an example. The authors present a case study with a minimum buffer level of10 % of the total buffer size. The definition of buffer levels may depend on specificrequirements of the service. In the proposed system, with video on demand, a clientbuffer threshold of 35 % provides robustness to the system.

If the buffer occupancy percentage is greater than the established threshold, theadaptation module selects the next segment to request according to the available band-width. The selection must also take into account that the bitrate of the segment should belower than the estimated bandwidth.

The proposed estimation and adaptation modules can be easily integrated in a DASH client.Moreover, the whole adaptation system presents a non-intrusive solution to perform theadaptation process in a streaming architecture.

Fig. 2 Adaptation system

Multimed Tools Appl

4 Evaluation

4.1 Testbed

To evaluate the adaptation system presented in Section 3, we propose the same test environ-ment as that proposed in [20], consisting of a server, a DASH client and an NS-3 networkemulator to modify the conditions of available bandwidth in a controlled environment.

The DASH client (which includes an HTTP client and a player) is implemented in C++using the DASH reference software [19]. The client includes the estimation and adaptationalgorithm presented in Section 3. The client’s buffer size is set to 30 segments length forevaluation purposes.

The server runs an Apache2 web server. Video contents hosted on the web server wereobtained from DASH dataset published by Lederer et al. [14]. The contents of the selecteddatabase correspond to the Big Buck Bunny video with a resolution of 480p. Bitrates of thedifferent representations range from 100 Kbps to 4.5 Mbps. In HTTP streaming, the content isdivided into segments of short duration. Generally, the segment size is between 2 and 10 s [6].Recent studies evaluating the effect of different sizes of segment streaming transmission usingDASH conclude that small segment sizes optimize network performance [14]. For this reason,in our case, the segment size used is 2 s. Furthermore, short duration of the segments leads tofaster adaptation. Since our throughput prediction is based on previous throughput segments(as explained in Section 3), if the segment duration is long (10 s) the method is not feasible topresent bandwidth fluctuations [24].

The performance of the proposed system is analyzed in different contexts, includingcongested and non-congested scenarios. Variation patterns of available bandwidth are shownin Figs. 3, 4, 5, 6 and 7. The NS-3 emulation tool was used to modify the network conditions,with CBR background traffic to congest the link. Available bandwidth will be between 0.3 and6 Mbps.

5 Results

In order to obtain stable results, we carried out long-term experiments, with 10 repetitions.Figures 3, 4, 5, 6 to 7 show some examples of the graphical results of the estimation andadaptation process in the evaluation scenarios.

Fig. 3 Ramp-down model

Multimed Tools Appl

As described in Section 3, the client analyzes the MPD file and begins to request thecontent with the lowest quality. Once the buffer reaches a minimum level, the phase ofmonitoring the available bandwidth starts and the client analyzes the values of the metrics toestimate the available bandwidth. If there are free resources on the network, the client willrequest the next segment with a higher bitrate.

Figures 3 and 4 show that in cases with smooth changes in available bandwidth, thealgorithm does not make overestimates in the available capacity of the network, so the clientdoes not request segments with a bitrate higher than the available bandwidth.

Figures 3, 5 and 6 show the behavior of the system when the available bandwidth presentshigh values in the initial instants of transmission. In some cases, the client requests the segmentwith the lowest bitrate (100 Kbps) although the algorithm correctly estimates the availablebandwidth. This is because, at the beginning of the transmission, buffer levels can presentvalues below the established minimum threshold.

Figure 5 shows the instants at which there is a sudden drop in the availablebandwidth. It is possible that the client had already started downloading a segmentwith a higher bitrate than the bandwidth now available. Congestion will cause thedownload of the segment to take longer. The situation normalizes when the clientrequests the next segment with the appropriate bitrate.

Fig. 4 Ramp-up model

Fig. 5 Step model

Multimed Tools Appl

To assess the stability in the estimation process, we propose the scenarios of Figs. 5, 6 and 7,with step models. After the initial phase, while the available bandwidth remains constant, thealgorithm maintains the segment bitrate. This is an important characteristic for an adaptivealgorithm, since related works to subjective evaluations conclude that constants and unneces-sary changes in bitrate negatively affect the quality perceived by the user [18].

Since the video player will choose a level of quality that has a lower bitrate than theestimated bandwidth, the download rate will be higher than the playback rate. In this way, theclient requests the multimedia data that match the available bandwidth in the network, withoutcongesting the medium. In some cases, the difference between the available bandwidth and thesegment bitrate is almost 50 %, because of the safeguard introduced by the estimationalgorithm and the discrete available values of the bitrates of the segments. This also preventsrebuffering events and buffer starvation.

However, when there is an interruption or reduction in the bitrate, it is possible that thebuffer does not satisfy the minimum required level of occupation, so the algorithm enters thephase of rebuffering, as explained previously. During the rebuffering, the quality is degraded tothe lowest quality. By choosing the lowest quality for the rebuffering phase, we ensure that thisprocess is carried out in the shortest time possible. Nevertheless, cuts during playback of the

Fig. 6 Step model decreasing

Fig. 7 Step model increasing

Multimed Tools Appl

contents may occur in cases where the available bandwidth is reduced [26], due to thecomplete emptying of the client buffer.

Table 1 shows the results of the average, the variance and the 95 % confidence interval ofthe percentage of time that the buffer occupancy level is below the minimum threshold. Asshown in the numerical results, for ramp scenarios the values do not exceed 4 % of the totaltime. Step model increasing and step model decreasing scenarios present higher values.Finally, for the step model, almost half of the total time the buffer level is below the minimumthreshold, due to the extreme conditions regarding the reductions in available bandwidth ofthis scenario.

Figure 8 shows the smallest observation, lower quartile, median, upper quartile, largestobservation and, in some cases, the observations that present outlier values. Results arepresented in the same order of magnitude as the results of Table 1.

To complete the interpretation of these results, Table 2 and Fig. 9 summarize theaverage, variance and the 95 % confidence intervals and boxplot of the total time (%)that the playback of the contents freezes due to the complete emptying of the clientbuffer. For ramp models and for the step model increasing there are no cuts in theplayback time, as the client buffer is never depleted. For step model decreasing, someproblems are identified at the end of the simulation, where there is a high constraintin availability. The worst scenario is the step model, where service interruptions occurduring long intervals.

Table 3 and Fig. 10 show the total time (%) that the buffer is full. In this sense, for most of theevaluated scenarios, the percentage is above 25%. In cases where there are small changes in the

Table 1 Statistical parameters ofthe total time (%) that buffer occu-pancy level is below the minimumthreshold

Average Variance Confidence intervals

Ramp down model 1,82 0.05 [1.66–1.97]

Ramp up model 3,97 0.53 [3.45–4.49]

Step model 55,02 23.43 [51.56–58.48]

Step model decreasing 15,16 14.57 [11.96–18.35]

Step model increasing 36,15 11.66 [33.26–39.57]

Fig. 8 Boxplot of the total time (%) that buffer occupancy level is below the minimum threshold

Multimed Tools Appl

available bandwidth, these results guarantee the continuous playback of the contents. For thesereasons, the step model presents the lowest total time of full occupancy in the client buffer.

6 Comparison between the proposed DASH system and RTP streaming with SVC

In the field of Internet video transmission, there are two clearly differentiated trends:on the one hand there are the traditional solutions based on UDP, and on the other,recently new proposals employing TCP as the transport mechanism have beenincluded.

In previous studies we analyzed the processes of estimation and adaptation withRTP as the video transport protocol. In a first approach, the adaptation of the contentswas performed by real-time transcoding [9]. The improvements applied to the initialsystem focused on the use of SVC technology, thus avoiding overloading the server[20]. Both proposals employed feedback using RTCP packets to provide informationabout the transmission status from client to server. The metrics for estimating avail-able bandwidth are based on the packet loss ratio, jitter and the linearity of receptiontimes of RTP packets.

One advantage of SVC is the possibility of distributing information in multiple layers withminimal redundancy. This makes it efficient in terms of storage with various levels of quality.The disadvantage of SVC is the complexity in generating the streams and the restrictionsimposed by the codec, meaning that the adoption of SVC has been slower. Adaptation results

Table 2 Statistical parameters ofthe total time (%) freeze playback Average Variance Confidence intervals

Ramp down model 0 0 [NaN—NaN]

Ramp up model 0 0 [NaN—NaN]

Step model 38.52 78.91 [32.16–44.87]

Step model decreasing 3.34 3.70 [1.54–5.10]

Step model increasing 0.004 0.0001 [−0.005–0.01]

Fig. 9 Boxplot of the total time (%) freeze playback

Multimed Tools Appl

of our proposed SVC-based system conclude that the selection of transmission layers is carriedout quickly and accurately.

In terms of scalability, the SVC-based solution presents better results than the transcodingoptions. However, there are still limitations in this field, because the server needs to maintain acontrol session for each client. Also, today’s networks are built using CDNs and firewalls,many of which do not support the RTP streaming.

HTTP avoids these problems. In this paper, the estimation and adaptation proposal iscarried out employing the DASH technology, changing the approach of previous work andmoving the adaptation logic to the client. In this case, the estimation process is based ondifferent metrics such as estimated throughput of the previous segments and the level ofoccupancy of the client buffer, as previously explained.

Both cases employ non-intrusive bandwidth estimation techniques on the client’s side. Inthe RTP-based solution, the metric values are communicated to the server via RTCP packets.In the DASH-based solution, the client uses the information of the metrics to make decisionsabout changing the bitrate of the requested stream.

Table 4 summarizes the characteristics of each system.Both technologies are totally different and must be compared from the point of view of

perceived quality. For that purpose, we used the same evaluated scenarios as presented in [20].The network access link will have a capacity of 10 Mbps and CBR traffic will follow differenttendency models, with a maximum value of 6 Mbps, so the available bandwidth will bebetween 4 and 10 Mbps.

We have used the DASHEncoder [14] to encode the same video content as used in[20] with the same rate, but adapting the features to the DASH technology. Thesource sequence, with CIF resolution and named “Bridge”, is obtained from the videotest repositories.4 The characteristics of the SVC video are shown in Table 5. Theencoded video includes two types of SVC scalability: temporal and quality. In termsof temporal scalability we have 2 layers: T0 and T1, with values of 15 and 30 fpsrespectively. At the same time, we can add up to 3 extra levels of quality scalability(from Q0 to Q3) at each temporal level.

Figures 11 and 12 show an example of the adaptation process for the same scenario in theSVC-based system and in the DASH system. The graphical examples show the differentbehavior of each adaptation algorithm.

In the SVC system, the server performs the adaptation process from the informationcontained in the RTCP-APP packet. The client sends the metrics to estimate the available

4 http://trace.eas.asu.edu

Table 3 Statistical parameters ofthe total time (%) that buffer occu-pancy level is 100 %

Average Variance Confidence intervals

Ramp down model 52,06 20.79 [48.79–55.32]

Ramp up model 56,46 2.37 [55.36–57.56]

Step model 6,63 0.84 [5.97–7.29]

Step model decreasing 43,98 11.74 [41.53–46.44]

Step model increasing 25,56 37.76 [21.16–29.95]

Multimed Tools Appl

bandwidth in the RTPC-APP packets every 5 s. Therefore overestimation may occur at theinstants in which the capacity of the network is decreasing and the metrics have not beenupdated.

The proposed estimation and adaptation module for DASH produces estimations ofavailable bandwidth for each segment request. Therefore, changes in network capacity aredetected earlier. However, the use of the free resources in the network is not as efficient as inthe RTP scenarios. As mentioned before, TCP streaming requires more available bandwidth toproperly transmit the same contents, and our proposed system includes a safeguard of 20 % ofthe estimated bandwidth. As a result, the selected bitrate in the DASH system is always lower(Figs. 11 and 12).

Adaptive systems also have to take into account the perceived quality. Alvarezet al. [3] have conducted subjective experiments to assess the preferences towards anadaptive system such as the one presented in [20]. The results show that the adaptivesystem improves the video quality in the majority of situations. Furthermore, usersprefer a reduction in quality rather than a reduction in temporal layers. In this sense,the proposed DASH system only implements a reduction in quality to accomplish theadaptation process.

There are several metrics to estimate the expected subjective quality. The mostextended metric to evaluate video quality is the Peak Signal-to-Noise Ratio (PSNR).We make use of this video quality metric to study the transmission of multimedia

Table 4 Characteristics of evaluated systems

SVC-based system DASH system

Transport protocol RTP/UDP HTTP/TCP

Adaptation logic Server Client

Feedback mechanism RTCP −Estimation metrics Packet loss, jitter, linearity of reception

times of RTP packetsThroughput, buffer level

Fig. 10 Boxplot of the total time (%) that buffer occupancy level is 100 %

Multimed Tools Appl

content from an end-to-end perspective. Table 6 summarizes the results of PSNR foreach scenario. Two new models (triangular models) are included to reproduce allconditions evaluated in [20]. Results show that the proposed estimation and adaptationsystem based on DASH performs better in terms of received video quality comparedto the SVC-based system, and in some cases, the improvement obtained over SVC is6 dB. Since the SVC-based system can introduce packet losses, as it uses UDP as atransport protocol, the PSNR metric could present lower values although the trans-mission bit-rate is higher.

However, despite the fact that the quality perceived by the user is slightly improved,another issue to consider are the overheads introduced by TCP and HTTP. Compared toRTP, HTTP imposes much more overheads [16]. Our results show that, under the sameconditions, the DASH system increases the total traffic by 2.91 %. Furthermore, as wementioned before, the bandwidth utilization in congested networks is not as good as in theSVC-based system. So SVC adaptive systems and DASH systems have both advantages anddisadvantages that network managers have to take into account when deploying a streamingservice.

7 Conclusions

The Internet best-effort service imposes a set of challenges for audio and video transmission.Developing additional mechanisms to avoid congestion processes is necessary, particularly inaccess networks.

Table 5 SVC Video propertiesused in the test T0 T1

Q0 2.9 Mbps 4 Mbps

Q1 3.6 Mbps 5.4 Mbps

Q2 4.2 Mbps 6.5 Mbps

Q3 5.3 Mbps 8.6 Mbps

Fig. 11 Ramp-down model

Multimed Tools Appl

HTTP streaming has gained popularity because of the benefits presented compared withtransmission based on UDP, such as the facility of deployment over the existing infrastructure.The trend today is the delivery of multimedia content in segments using HTTP. However, theestimation and adaptation process to the available bandwidth is still necessary and is usuallyoutside the scope of the standard.

This paper presents the design, implementation and analysis in a real environment of anestimation and adaptation module for DASH system. The analysis of the proposed solutionshows that the estimation algorithm does not perform overestimations of the available band-width. The results have demonstrated that, in most situations, the video quality is improvedcompared to previous solutions based on SVC. In addition, occupancy levels in the clientbuffer remain stable, so that small interruptions or reductions in the available bandwidth do notaffect the perceived quality. The proposed adaptation and estimation module for DASHperforms an accurate and faster adaptation to changes than the SVC-based system. Moreover,the designs of the estimation and adaptation algorithms for DASH are much simpler than thosefor an SVC-based system. In this sense, the proposed DASH system simplifies the adaptationlogic compared with the previous SVC system. However, RTP uses network resources in amore efficient manner and the generated traffic is almost 3 % less under the same networkconditions. In addition, in the DASH system, the storage capacity on the server side has to beincreased, especially if a small segment size is used.

Our future work includes the subjective evaluation of the proposed DASH system and thestudy of new metrics that improve the performance of the algorithm. Also more complex

Fig. 12 Step model

Table 6 PSNR values (dB) foreach system PSNR SVC

system (dB)PSNR DASHsystem (dB)

Ramp down model 43,3090 41,3952

Ramp up model 39,9105 40,4593

Step model 40,2943 41,2373

Step model decreasing 41,4267 41,1949

Step model increasing 36,7324 40,3380

Triangular model decreasing 34,7573 40,9498

Triangular model increasing 39,6710 40,8126

Multimed Tools Appl

network models have to be taken into account, including architectures with Content Distribu-tion Networks (CDNs) to exploit the advantages of HTTP streaming in such environments.The impact on the generated traffic must be evaluated in architectures with caching techniques,analyzing the behavior of our system when different clients request different quality segments.The extension of the architecture proposed in this paper should also focus on the use ofscalable codecs and the design of the segment selection algorithm for such system.

Acknowledgments This work was partially supported by the University of Oviedo and the Principality ofAsturias through the SV-PA-13-ECOEMP-75 project.

References

1. Akhshabi S, Begen AC, Dovrolis C (2011) An experimental evaluation of rate-adaptation algorithms inadaptive streaming over http. Proceedings of the second annual ACM conference on Multimedia systems,MMSys 157–168. doi: 10.1145/1943552.1943574

2. Akhshabi S, Narayanaswamy S, Begen AC, Dovrolis C (2012) An experimental evaluation of rate-adaptivevideo players over HTTP. Signal Process Image Commun 27(4):271–287. doi:10.1016/j.image.2011.10.003

3. Alvarez A, Pozueco L, Cabrero S, Pañeda XG, Garcia R, Melendi D, Orueta GD (2013) Subjectiveevaluation of critical success factors for a QoE aware adaptive system. Comput Commun 36(15–16):1608–1620. doi:10.1016/j.comcom.2013.07.005

4. Andelin T, Chetty V, Harbaugh D, Warnick S, Zappala D (2012) Quality selection for dynamic adaptivestreaming over http with scalable video coding. Proceedings of the 3rd Multimedia Systems Conference,MMSys 149–154. doi: 10.1145/2155555.2155580

5. Arsan T (2012) Review of bandwidth estimation tools and application to bandwidth adaptive videostreaming. 9th International Conference on High Capacity Optical Networks and Enabling Technologies,HONET 152–156. doi: 10.1109/HONET.2012.6421453

6. Begen A, Akgul T, Baugher M (2011) Watching video over the web: part 1: streaming protocols. IEEEInternet Comput 15(2):54–63. doi:10.1109/MIC.2010.155

7. Biernacki A, Tutschku K (2013) Performance of HTTP video streaming under different network conditions.Multimedia Tools and Applications, 1–24. doi: 10.1007/s11042-013-1424-x

8. Famaey J, Latré S, Bouten N, Van de Meerssche W, De Vleeschauwer B, Van Leekwijck W, De Turck F(2013) On the merits of SVC-based HTTP adaptive streaming. 12th IFIP/IEEE International Symposium onIntegrated Network Management, IM 419–426

9. Fraga A, Pozueco L, Pañeda XG, García R, Melendi D, Cabrero S (2011) A non-intrusive estimation forhigh-quality internet TV services. Multimed Tools Appl 54(3):569–588. doi:10.1007/s11042-010-0566-3

10. Huang TY, Handigol N, Heller B, McKeown N, Johari R (2012) Confused, timid, and unstable: picking avideo streaming rate is hard. Proceedings of the 2012 ACM conference on Internet measurement conference,IMC 225–238. doi: 10.1145/2398776.2398800

11. ISO/IEC (2012) Information technology—dynamic adaptive streaming over HTTP (DASH)—part 1: mediapresentation description and segment formats. ISO/IEC 23009–1:2012

12. Kantarcı A (2008) Streaming of scalable h.264 videos over the Internet. Multimed Tools Appl 36(3):303–324. doi:10.1007/s11042-007-0147-2

13. Kofler I, Kuschnig R, Hellwagner H (2012) Implications of the ISO base media file format on adaptiveHTTP streaming of H. 264/SVC. Consumer Communications and Networking Conference, CCNC 549–553.doi: 10.1109/CCNC.2012.6180986

14. Lederer S, Müller C, Timmerer C (2012) Dynamic adaptive streaming over HTTP dataset. Proceedings ofthe 3rd Multimedia Systems Conference, MMSys 89–94. doi: 10.1145/2155555.2155570

15. Liu C, Bouazizi I, Gabbouj M (2011) Rate adaptation for adaptive HTTP streaming. Proceedings of thesecond annual ACM conference on Multimedia systems, MMSys 169–174. doi: 10.1145/1943552.1943575

16. Lohmar T, Einarsson T, Frojdh P, Gabin F, Kampmann M (2011) Dynamic adaptive HTTP streaming of livecontent. IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks,WoWMoM 1–8. doi: 10.1109/WoWMoM.2011.5986186

Multimed Tools Appl

17. Miller K, Quacchio E, Gennari G, Wolisz A (2012) Adaptation algorithm for adaptive streaming over HTTP.Packet Video Workshop, PV 173–178. doi: 10.1109/PV.2012.6229732

18. Mok RKP, Luo X, Chan EWW, Chang RKC (2012) QDASH: a QoE-aware DASH system. Proceedings ofthe 3rd Multimedia Systems Conference, MMSys 11–22. doi: 10.1145/2155555.2155558

19. Müller C, Lederer S, Pöcher J, Timmerer C (2013) libdash—an open source software library for the MPEG-DASH Standard. Proceedings of the IEEE International Conference on Multimedia and Expo Workshops,ICMEW 1–2. doi: 10.1109/ICMEW.2013.6618220

20. Pozueco L, Pañeda XG, García R, Melendi D, Cabrero S (2013) Adaptable system based on scalable videocoding for high-quality video service. Comput Electr Eng 39(3):775–789. doi:10.1016/j.compeleceng.2013.01.015

21. Sánchez de la Fuente Y, Schierl T, Hellge C, Wiegand T, Hong D, De Vleeschauwer D, Van Leekwijck W,Le Louédec Y (2011) iDASH: improved dynamic adaptive streaming over HTTP using scalable videocoding. Proceedings of the second annual ACM conference on Multimedia systems, MMSys 257–264. doi:10.1145/1943552.1943586

22. Schierl T, Sanchez de la Fuente Y, Globisch R, Hellge C, Wiegand T (2010) Priority-based media deliveryusing SVC with RTP and HTTP streaming. Multimed Tools Appl 55(2):227–246. doi:10.1007/s11042-010-0572-5

23. Sodagar I (2011) The MPEG-DASH standard for multimedia streaming over the internet. IEEE MultiMedia18(4):62–67. doi:10.1109/MMUL.2011.71

24. Thang TC, Ho Q-D, Kang JW, Pham AT (2012) Adaptive streaming of audiovisual content using MPEGDASH. IEEE Trans Consum Electron 58(1):78–85. doi:10.1109/TCE.2012.6170058

25. Tian G, Liu Y (2012) towards agile and smooth video adaption in dynamic HTTP streaming. Proceedings ofthe 8th international conference on Emerging networking experiments and technologies, CoNEXT 109–120.doi: 10.1145/2413176.2413190

26. Wang B, Kurose J, Shenoy P, Towsley D (2008) Multimedia streaming via TCP: an analytic performancestudy. ACM Trans Multimed Comput Commun Appl 4(2):1–22. doi:10.1145/1352012.1352020

27. Wu D, Hou YT, Zhu W, Lee H-J, Chiang T, Zhang Y-Q, Chao HJ (2000) On end-to-end architecture fortransporting MPEG-4 video over the Internet. IEEE Trans Circ Syst Video Technol 10(6):923–941. doi:10.1109/76.867930

28. Zhou C, Zhang X, Huo L, Guo Z (2012) A control-theoretic approach to rate adaptation for dynamic HTTPstreaming. IEEE Visual Communications and Image Processing, VCIP 1–6. doi: 10.1109/VCIP.2012.6410740

Laura Pozueco has anMD in Telecommunication Engineering from the University of Oviedo. Currently she is aPhD student in the research group of Distributed Multimedia Services in the same university. She is alsocertificated in several CISCO technologies (CCNA, Securing Networks with Cisco Routers and Switches,Securing Networks with PIX and ASA and Advanced Wireless LAN for Field Engineers).

Multimed Tools Appl

Xabiel García Pañeda is a Computer Science Engineer and PhD from the University of Oviedo. He is anAssociate Professor with the Department of Computer Science of the University of Oviedo and member of theSYMMWorking Group of the W3C. His current research interests are in the area of multimedia systems, digitalinteractive TV services and mobile ad-hoc networks.

Roberto García has a Ph.D. degree from the University of Oviedo and a Telecommunication Engineering degreefrom The Technical University of Madrid. He is an Associate Professor with the Department of ComputerScience, University of Oviedo. His current research interests are in the area of telecommunication networks andservices, applied to the performance analysis, modeling and simulation of multimedia services.

Multimed Tools Appl

David Melendi is a Computer Science Engineer from the University of Oviedo with a PhD from the Universityof Oviedo. Nowadays, he is an Associate Professor with the Department of Computer Science of the Universityof Oviedo. His current research interests are in the area of multimedia systems and services, in contentdistribution networks and digital TV services.

Sergio Cabrero is a Telecommunication Engineer from the University of Oviedo and a PhD student. He is alsoan Assistant Professor with the Department of Computer Science of the University of Oviedo. His currentresearch interests are in the area of telecommunication networks, digital TV, multimedia services and mobile ad-hoc networks.

Multimed Tools Appl

Gabriel Díaz has a PhD in Physics. He works as associate professor at the Electrical and Computer EngineeringDepartment of the Spanish University for Distance Education (UNED). He is the director of the ElectricElectronics and Industrial Control Master at UNED. His research interests include the different approaches forgetting the best of ICT technologies applied to different kinds of security and electronics learning for HigherEducation at universities, information security in communications, security measurement and metrics andsecurity for Industrial Process Control Systems. He is a Senior Member of the IEEE and chair of SpanishChapter of the IEEE Education Society.

Multimed Tools Appl