Quality-Aware Bandwidth Allocation for Scalable On-Demand Streaming in Wireless Networks

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Quality-Aware Bandwidth Allocation for Scalable

On-Demand Streaming in Wireless Networks

Jen-Wen Ding, Der-Jiunn Deng, Tin-Yu Wu, and Hsiao-Hwa Chen

Abstract—In this paper, we propose a scalable transport

scheme for delivering on-demand video streams over broadband wireless networks in next-generation network/IP multimedia sub-system (NGN/IMS) architecture. The proposed transport scheme makes use of fine-granular-scalability (FGS) encoded videos to accommodate high bandwidth variation of wireless networks. We formulate the bandwidth allocation to FGS-encoded streams as a resource allocation problem and develop a quality-aware bandwidth allocation scheme, called QABA. With QABA, the proposed transport scheme can dynamically adjust the bit rate allocated to different streams to maximize the overall perceptual quality when available network bandwidth varies with time. QABA is theoretically proven to be able to find the optimal bandwidth allocation for all on-demand streams. To validate the effectiveness of QABA, extensive trace-based simulations are performed.

Index Terms—Bandwidth allocation, video streaming, video

broadcasting, MPEG-4 FGS video, NGN/IMS.



APID advances in wireless networks and multimedia technologies lead to the creation of many innovative multimedia applications. One of the most attractive applica-tions is on-demand video streaming. However, it is challenging to provide high quality on-demand streaming over wireless networks for the following reasons.

1) Limited bandwidth: Bandwidth is a scarce resource in

wireless networks. For example, although cellular networks have evolved from the first generation to the third generation technology, the available throughput of cellular networks is still constrained compared to that of wired networks. For instance, the maximum bandwidth of Gigabit Ethernet is about 1000 Mbps, while that of High-Speed Downlink Packet Access (HSDPA), a 3.5G technology, is about 14.4 Mbps. On the other hand, video streaming has a huge bandwidth requirement. A video stream usually requires a few hundreds to a few thousands kbps.

2) Bandwidth Competition: In wireless networks, all users

within the same radio range of a base station (or access point) need to compete with each other for the limited available bandwidth. However, due to the huge bandwidth requirement

Manuscript received 1 March 2009; revised 20 October 2009.

Jen-Wen Ding is with the Department of Information Management, Na-tional Kaohsiung University of Applied Sciences, Taiwan (e-mail: jwd-ing@cc.kuas.edu.tw).

Der-Jiunn Deng is with the Department of Computer Science and Informa-tion Engineering, NaInforma-tional Changhua University of EducaInforma-tion, Taiwan (e-mail: djdeng@cc.ncue.edu.tw).

Tin-Yu Wu is with the Department of Electrical Engineering, Tamkang University, Taiwan (e-mail: tyw@mail.tku.edu.tw).

Hsiao-Hwa Chen is with the Department of Engineering Science, National Cheng Kung University, Taiwan (e-mail: hshwchen@ieee.org).

Digital Object Identifier 10.1109/JSAC.2010.100408.

of video streaming, it is usually more cost-effective to assign higher transmission priorities to other applications that require much less bandwidth, such as voice over IP (VoIP) and short message service (SMS). From this point of view, the available network resources tend to vary with time for video streaming applications, although various wireless networks have em-ployed different quality-of-service (QoS) support mechanisms [1-2].

3) Channel Requirement: For live streaming, all users

requesting the same video can share the same multi-cast/broadcast channel. However, for on-demand streaming, all users requesting the same video cannot share the same mul-ticast/broadcast channel since the users may watch different parts of the video.

To overcome the aforementioned difficulties, we designed wireless video-on-demand (VoD) streaming systems using two principles. First, on-demand streaming service is limited to popular contents (such as movies, dramas, news, and sports). Second, periodic video broadcasting protocols are employed to largely reduce the high bandwidth requirement of VoD services. With periodic broadcasting protocols, a few channels can provide VoD services to a large number of users [3]. Rep-resentative examples of periodic VoD broadcasting protocols include Fast Broadcasting [4], Pagoda Broadcasting [5], RFS Broadcasting [6], MHB Broadcasting [7], CAR Broadcasting [8], UHB Broadcasting [9], etc. It has been shown that with MHB broadcasting [7], for a 90-minute video program with five broadcast channels, the startup latency for demanding a stream is about one minute. Section II provides the necessary background knowledge for VoD broadcasting protocols.

In this paper, we propose a scalable transport scheme for delivering on-demand streams over broadband wireless networks in next-generation network/IP multimedia subsystem (NGN/IMS) architecture. We adopt this architecture because it is expected to be widely adopted by both wireline and wireless service providers to support the next wave of multimedia communication applications [10-11]. Our proposed transport scheme makes use of fine-granular-scalability (FGS) encoded videos because much research has shown that FGS-encoded streams have great adaptability to network bandwidth variation [12-15]. To maximize the overall perceptual quality of all video streams, we propose a quality-aware bandwidth allo-cation algorithm, called quality-aware bandwidth alloallo-cation (QABA), for on-demand streams over broadband wireless networks. In the next subsection, we review recent studies related to our work.


A. Related Works

Much research has been devoted to adaptive video stream-ing. The goal of adaptive streaming is to dynamically adjust the content of video streams to handle the variation of different resource constraints imposed by networks and user devices, such as network bandwidth and transmission power/display capability/computing speed of user devices. According to the video format employed, the techniques for adaptive streaming can be classified into three categories: scalable coding, video transcoding, and stream replication. Scalable coding can be further divided into three sub-categories: layered coding, FGS coding, and multiple description coding (MDC). With layered coding, a video sequence is encoded into one base stream and multiple enhancement streams. While the base stream can provide the basic visual quality, the enhancement streams can improve the visual quality in a progressive way. Many streaming techniques for layered coded streams can be found in the literature [16-19]. In [16], the authors studied a layered video multicast system over wired and wireless networks, and proposed a joint bandwidth and forward error correction (FEC) allocation scheme for layered streams to optimize the overall video quality, subject to a certain loss rate requirement. In [17], a dual-plan bandwidth smoothing scheme was proposed by taking advantage of the signal-to-noise ratio (SNR) scal-ability of layered coding. When the renegotiation for a new transmission bit rate fails, the proposed scheme can adaptively discard certain enhancement layers to guarantee continuous video playback at the original frame rate.

Unlike layered coding, FGS coding generates one base stream and only one enhancement stream. With layered cod-ing, it is necessary for a user to receive a complete enhance-ment stream to decode it. On the contrary, with FGS coding, a receiver can receive and decode only part of the enhancement stream. Therefore, FGS coding provides much better adapt-ability to bandwidth variation than layered coding. In [13], an optimal rate-allocation scheme for FGS-encoded streams was proposed to achieve constant-quality reconstruction of frames under a dynamic rate budget constraint. In [14], the authors removed the assumption used in [13] that the rate-distortion (RD) characteristics of all video frames are identical. Also, they proposed a rate-control algorithm for FGS-encoded streams which has a low delay and achieves a good tradeoff between the average visual distortion and quality fluctuation. In [15], a network-aware and source-aware streaming system was proposed to transmit MPEG-4 FGS encoded videos within single-cell and multicell IEEE 802.11 networks. The proposed system can choose the optimal channel coding rate and bandwidth in both uplink and downlink, and perform FEC transcoding in the server located at the access point. Yet some other studies focused on designing adaptive error control for FGS-encoded videos [20-22].

With MDC, a video sequence is encoded into multiple independent streams, each of which can be decoded indepen-dently. Compared to layered coding and FGS coding, MDC has the best robustness but the lowest coding efficiency. MDC is usually applied to multi-path/multi-source streaming, such as peer-to-peer streaming and streaming over mobile ad hoc networks [23-24].

Video transcoding translates video streams from one format to another (e.g., from MPEG-2 to MPEG-4) to meet the resource variation. The main drawback of video transcoding is its high computational complexity. Various transcoding tech-niques have been proposed to provide tradeoff between com-putational complexity and reconstructed video quality. [25] provided a good review of various techniques and research issues regarding video transcoding. Stream replication gen-erates replicated streams of different rates to serve receivers of diverse access bandwidths. Two important design issues regarding stream replication, how to choose an appropriate number of streams and how to allocate bandwidth to the streams, were carefully studied in [26].

In addition to the studies mentioned above, an emerging design trend for adaptive streaming is cross-layer design, which tries to improve the performance of streaming by designing multiple protocol layers jointly. However, cross-layer design may increases the design complexity significantly. Many studies on cross-layer streaming have been proposed, most of which focused on integrating different parts (param-eters) across layers [27-29].

In summary, for the techniques described above, the tra-ditional layer-based approached are unable to provide fine-granular bandwidth adaptability, MDC-based approach has a poor coding efficiency, stream-replication-based approach consumes too much bandwidth and storage, and transcoding-based approach has a high computational complexity and may result in a long response time. Although FGS-based approach is suitable for adaptive streaming and much work on it has been done, little research has been devoted to the resource allocation for on-demand streaming using FGS coding.

B. Our Contributions

The contribution of this paper is twofold. First, we pro-pose a scalable transport scheme for on-demand streaming over broadband wireless networks in NGN/IMS architecture. The proposed transport scheme combines VoD broadcasting protocols and FGS-encoded video so that it can largely reduce the bandwidth requirement of on-demand streaming and can accommodate high bandwidth variation of wireless networks. Second, we formulate the bandwidth allocation for on-demand streaming as a resource allocation problem, and develop an optimal bandwidth allocation algorithm, QABA, which makes use of rate-distortion characteristics of video sequences to maximize the overall perceptual quality of all users when the available bandwidth varies with time. To the best of our knowledge, this study is the first attempt to discuss quality-aware bandwidth allocation for on-demand streaming over wireless networks.

The rest of this paper is organized as follows. Section II describes the system architecture used in this paper and provides an overview of necessary background knowledge. Section III constructs a mathematical model for the bandwidth allocation problem and develops an optimal bandwidth allo-cation algorithm. Section IV presents the simulation results, followed by the conclusions made in Section V. The appendix details the proof of the optimality of the QABA algorithm.


Fig. 1. A simplified view of scalable on-demand streaming in NGN/IMS architecture.


A. System Architecture

In this section, we describe the system architecture for wireless on-demand streaming in NGN/IMS architecture [10-11]. As shown in Fig. 1, the system consists of four compo-nents: video server, VoD stream broadcaster, adaptive stream shaper, and user equipment. The video server can be placed in an external third-party IP-based network or in an IMS home network. The VoD stream broadcaster and adaptive stream shaper are located in the network element called media resource function (MRF). In IMS architecture, MRF is responsible for providing media related functions. The stream broadcaster is used to transmit FGS-encoded video streams using VoD broadcasting protocols (such as Fast Broadcasting or Pagoda Broadcasting). The adaptive stream shaper is used to dynamically reshape the broadcast streams according to bandwidth variation in access networks. With the use of stream shaper, the system can maximize the overall perceptual quality of all streams for a given available bandwidth. In what follows, we briefly review VoD broadcasting protocols and the important features of FGS-encoded videos.

B. VoD Broadcasting Protocols

To provide VoD streaming to a large number of users with limited network bandwidth, much research has been devoted to VoD broadcasting protocols [3]. Most of the VoD broadcasting protocols proposed in the literature share the similar design principles, such as Fast Broadcasting [4], Pagoda Broadcasting [5], RFS Broadcasting [6], etc. These schemes divide a video file into a series of segments, and then repeatedly broadcast the segments with different frequencies on different broadcast channels. When a user is watching a video segment, it is guaranteed that the next video segment can be received in time and the entire stream can be played back continuously. The user startup latency caused by a VoD broadcasting protocol ranges from a few seconds to a few minutes, depending on the design of the protocol and the total amount of bandwidth allocated for broadcasting.

In this paper, for ease of exposition, we employ Fast Broadcasting (FB) as the VoD broadcasting protocol [4].

Fig. 2. An example of FB scheme.

FB is a simple but efficient protocol. Notably, our design is orthogonal to VoD broadcasting protocols. The proposed transport scheme and bandwidth allocation algorithm can be easily extended to other VoD broadcasting protocols, such as Pagoda Broadcasting and RFS broadcasting. In what follows, we briefly review how FB works.

Let k denote the total number of broadcast channels for a video. The channels are indexed from 1 to k. FB first par-titions the video into n equal-sized segments s1, s2,· · · , sn,

where n = 2k − 1. Then, FB repeatedly transmits segments

s2i−1, s2i−1+1, s2i−1+2,· · · , s2i−1 on broadcast channel i.

The broadcasting period of channel i, denoted by Ti, is 2i−1

time slots. A time slot is defined as the length of the time to broadcast a video segment. Specifically, a time slot equals

L/n, where L is the length of the whole video. Because a user can only watch a video after it receives the beginning of the first video segment, the maximum startup latency for a user is one time slot, which equals T1. It can be observed that, while the number of broadcast channels increases linearly, the number of video segments increases exponentially and the startup latency decreases exponentially. Fig. 2 shows a simple example of FB, where k = 3 and n = 7.

C. FGS Encoded Videos

To support dynamic bit-rate adaptation for video streams, much research has been conducted on scalable coding in the past decade [12, 30-33]. Layered coding supports three types of scalability: SNR scalability, spatial scalability, and temporal scalability. With the layered coding, a video stream is compressed into one base-layer (BL) stream and multiple enhancement-layer (EL) streams. While the BL stream can provide the basic visual quality, the EL streams can improve the visual quality in a progressive way. The BL stream can be decoded independently, but the EL streams must be decoded together with the BL stream. The main drawback to layered coding is that each EL stream must be completely received; a partially received EL stream cannot be decoded. To further improve the adaptability to network bandwidth variation and error resiliency, FGS coding has been proposed as part of the MPEG-4 standard [20]. With the FGS coding, a video stream is compressed into one BL stream and only one EL stream. Similar to the layered coding, the BL stream must be completely received to be decoded. The BL stream is typically encoded using a low bit rate to accommodate a wide range of heterogeneous receivers. Due to its importance and small size, the BL stream is usually transmitted over a QoS-guaranteed


Our future work will focus on the resource allocation for H.264/SVC (scalable video coding) streams. The SVC extension of H.264/MPEG-4 AVC coding standard is the latest video coding standard developed by the ISO/IEC Moving Picture Experts Group (MPEG) and the ITU-T Video Coding Experts Group (VCEG). SVC has been designed to provide high scalability (temporal, spatial, and fidelity scalability) as well as high coding efficiency to cope with user and network heterogeneity. It still is an open research issue how to optimally allocate network bandwidth to different layers of SVC streams such that the perceptual quality of all users can be maximized. The work is underway to extend our current study to H.264/SVC streams.


PROOF OF THEOPTIMALITY OFQABA ALGORITHM Without loss of generality, let us assume that all EL seg-ments broadcasted during time slot t, se

t,1, set,2,· · · , set,K, are

re-indexed such that their quality reward functions are ordered in non-increasing order, i.e.,

Ct,1≥ Ct,2≥ · · · ≥ Ct,K

In the following text, we show that the modeled bandwidth allocation problem exhibits two properties, which lead to a greedy solution [38].

A. Greedy-choice property

Our first step is to prove that there exists an optimal bandwidth allocation policy that makes the first greedy choice, i.e., xt,1 = xmaxt,1 . Assume that A(K)opt is an optimal

band-width allocation policy, and the solution of A(K)opt is O(K) =


t,1, xot,2, xot,3,· · · , xot,K}. There are two cases to consider.

First, if A(K)opt sets xot,1= xmaxt,1 , then we are done. Second, if

A(K)opt sets xo

t,1< xmaxt,1 , then we can take away xmaxt,1 − xot,1

from the remaining bandwidth B, and reset xt,1 = xmaxt,1 ,

which yields a new allocation policy, A(K)g . The solution

of A(K)g is denoted by G(K) = {xgt,1, x g t,2, x g t,3,· · · , x g t,K}.

Because the reward function for the first greedy choice,

xgt,1 = xmax

t,1 , is the largest reward function, and because the

reward functions are linear functions of xs, the total reward of A(K)g is as good as A(K)opt (or else there is a contradiction),

and we are done.

B. Optimal substructure property

Our second step is to prove that an optimal solution to the bandwidth allocation problem contains the optimal solutions to its subproblems. Let P(K)denote the bandwidth allocation problem of size K, i.e., to find allocated bit rates for K EL broadcast channels. Consider that, after the first greedy choice has been made, i.e., xt,1 = xmaxt,1 , we obtain a

subproblem P(K−1) of the original problem P(K). P(K−1) is essentially the same bandwidth allocation problem with the decision variables {xt,2, xt,3,· · · , xt,K} and the new

band-width constraint B− xmax

t,1 . We need to prove the subsolution

of A(K)g , i.e., G(K−1) = {xgt,2, x g

t,3,· · · , x g

t,K}, is the optimal

solution to subproblem P(K−1). To prove this, we make the

opposite assumption, or G(K−1) = {xgt,2, x g

t,3,· · · , x g t,K} is

not an optimal solution for P(K−1). Let solution Q(K−1) =

{xQ t,2, x


t,3,· · · , x Q

t,K} be an optimal solution of

subprob-lem P(K−1). Let solution Q(K) = {xQt,1 = x max t,1 } ∪ {xQ t,2, x Q t,3,· · · , x Q

t,K}. Then Q(K) is a feasible solution to

the original problem P(K). However, because the solution

G(K−1) is not as good as Q(K−1), we have the following conclusion: Q(K) is better than G(K). Since we have proved that G(K)is as good as O(K) in the first step, it follows that

Q(K) is better than O(K). Since O(K) is assumed to be an optimal solution, this conclusion results in a contradiction. Hence G(K−1) = {xgt,2, x


t,3,· · · , x g

t,K} must be an optimal

solution for P(K−1), and we are done.

Using the above two properties, we can employ math-ematical induction to prove the optimality of QABA.


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Jen-Wen Ding is currently an Associate Professor

of the Department of Information Management, Na-tional Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. He received his B.S., M.S., and Ph.D. degrees in Engineering Science from National Cheng Kung University, Tainan, Taiwan, in 1996, 1998, and 2001, respectively. His research interests include multimedia communications and wireless networks. He has been invited to serve on technical program committee at many national and international conferences. He received the best paper award of Asia-Pacific Workshop on Visual Information Processing 2007 (VIP 2007) and Taiwan Academic Network Conference 2009 (TANET 2009).

Der-Jiunn Deng received the Ph.D. degree in

elec-trical engineering from National Taiwan University in 2005. He joined National Changhua Univer-sity of Education as an assistant professor in the Department of Computer Science and Information Engineering in August 2005 and then became an associate professor in February 2009. Dr. Deng visited Iowa State University, USA, in 2006. His research interests include multimedia communica-tion, quality-of-service, and wireless networks. Dr. Deng was the guest editor of the special issue on Ubiquitous Multimedia Computing: Systems, Networking, and Applications for the International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) and the guest editor of the special issue on Internet Resource Sharing and Discovery for the Journal of Internet Technology (JIT). He is serving on the editorial board of International Journal of Communication Systems (IJCS), International Journal of Wireless Networks and Broadcasting Technologies (IJWNBT), and Modeling and Simulation Magazine (SCS M&S Magazine). He also served on several technical program committees for IEEE and other international conferences. He is now serving as a program chair for the 2nd International Conference on Computer Science and its Applications (CSA-09). Dr. Deng is a member of the IEEE.

Tin-Yu Wu currently works as an Assistant

Pro-fessor in the Department of Electrical Engineering, Tamkang University, Taipei, Taiwan. He received his M.S., and Ph.D. degrees in the Department of Electrical Engineering, National Dong Hwa Univer-sity, Hualien, Taiwan in 2000 and 2007 respectively. His research interests focus on the next-generation Internet protocol, mobile computing and wireless networks.

Hsiao-Hwa Chen (S89-M91-SM00) is currently a

full Professor in the Department of Engineering Science, National Cheng Kung University, Taiwan. He obtained his BSc and MSc degrees from Zhe-jiang University, China, and a PhD degree from the University of Oulu, Finland, in 1982, 1985 and 1991, respectively. He has authored or co-authored over 250 technical papers in major international journals and conferences, five books and three book chapters in the areas of communications. He served as general chair, TPC chair and symposium chair for many international conferences. He served or is serving as an Editor or/and Guest Editor for numerous technical journals. He is the founding Editor-in-Chief of Wileys Security and Communication Networks Journal (www.interscience.wiley.com/journal/security). He is the recipient of the best paper award in IEEE WCNC 2008.


Fig. 2. An example of FB scheme.
Fig. 2. An example of FB scheme. p.3
Fig. 1. A simplified view of scalable on-demand streaming in NGN/IMS architecture.
Fig. 1. A simplified view of scalable on-demand streaming in NGN/IMS architecture. p.3