Peer-to-Peer Streaming of Layered Video: Efficiency, Fairness and Incentive Hao Huy, Yang Guo∗, and Yong Liuy yElectrical & Computer Engineering, Polytechnic Institute of NYU, Brooklyn, NY 11201 ∗ Corporate Research, Thomson, Princeton, NJ 08540 Abstract—Recent advance in scalable video coding (SVC) To adopt SVC into P2P streaming, two key design questions makes it possible for users to receive the same video with need to be answered: 1) layer subscription: how many layers different qualities. To adopt SVC in P2P streaming, two key each peer should receive; and 2) layer scheduling: how to design questions need to be answered: 1) layer subscription: how many layers each peer should receive? 2) layer scheduling: deliver to peers the layers they subscribed. From the system how to deliver to peers the layers they subscribed? From the point of view, the most efficient solution is to maximize the system point of view, the most efficient solution is to maximize aggregate video quality perceived by all peers, i.e, to optimize the aggregate video quality on all peers, i.e., the social welfare. the social welfare. From individual peer point of view, the From individual peer point of view, the solution should be fair. solution should be fair. However, in P2P streaming, due to Fairness in P2P streaming should additionally take into account peer contributions to make the solution incentive-compatible. In the dual server-consumer role of individual peers, the notion this paper, we develop utility maximization models to understand of fairness is much more subtle than that in traditional server- the interplay between efficiency, fairness and incentive in layered client systems, where clients are only considered as resource P2P streaming. We show that taxation mechanisms can be devised consumers. A solution allocating the same video quality to all to strike the right balance between social welfare and individual peers regardless of their contributions would not be considered peers’ welfare. We develop practical taxation-based P2P layered streaming designs, including layer subscription strategy, chunk as fair, and therefore would not provide incentives for peers to scheduling policy, and mesh topology adaptation. Through exten- contribute. A good layered P2P streaming solution has to strike sive trace-driven simulations, we demonstrate that the proposed the right balance between efficiency, fairness and incentive. designs can effectively drive layered P2P streaming systems to In this paper, we develop analytical models and practical converge to the desired operating points in a distributed fashion. streaming algorithms to understand and control the interplay between efficiency, fairness and incentive in layered P2P streaming. Specifically, we develop network-coding based util- I. INTRODUCTION ity maximization models to obtain the most efficient layered P2P live video streaming has recently emerged as a cost- streaming solution. The choice of peer utility function reflects efficient IPTV solution on the Internet. It has attracted sub- the target fairness among peers when they are considered only stantial attentions in research community and industry. Several as video consumers. To incorporate contribution-awareness, widely deployed commercial P2P live streaming systems [1]– we adopt taxation as a peer-incentive mechanism and augment [3] routinely attract hundreds of thousands of users to watch the utility maximization models to make the solution incentive- live video broadcast online. Extensive research [4]–[9] has compatible. studied various aspects of system design, ranging from P2P Taxation based incentive mechanism [14], [15] offers a overlay construction, data sharing strategy, adoption of ad- flexible framework that allows the tradeoff between users’ vanced video coding, to the application of network coding personal fairness/welfare and the system-wide social welfare technique. In-depth measurement [10]–[12] study further al- (see Fig. 1). Let ud be the upload bandwidth contributed by lows us to observe the working systems in the field. user d. Under a tax rate 0 ≤ t ≤ 1, the target received t PN The success of P2P computing hinges on the underlying video rate of user d is rd = (1 − t)ud + N i=1 ui, where principle that participants shall contribute their resources (in N is the total number of peers in the system. The received terms of bandwidth, storage space, or computational power) rate consists of two parts: a fraction of its own contribution, while enjoying the service. Most existing P2P streaming and a fair share from the pool of taxed bandwidth. The tax systems assume the cooperation of peers and deliver the same rate t adjusts the balance between individual peers’ welfare video quality to all peers. Recent advance in scalable video and the social welfare. As t approaches zero, the received coding (SVC) [13] makes it possible for users to receive video rate approach the contributed rate, mimicking the ‘tit- the same video with different qualities. SVC encodes video for-tat’ strategy. As t approaches one, the received video rate into correlated layers. The base layer can be independently is the same for all peers, thus achieve the social optimum as decoded, while higher layers are decodable only if layers described in Section II. beneath have been decoded. The video quality perceived by a Our contribution can be summarized as follows: user increases as the number of decoded layers increases. • We develop utility maximization models to study the 2 number of received substreams. The number of trees joined by a peer is dynamically adjusted to reflect the entitled video Service Substream differentiated Trading quality determined by the taxation policy. We analytically peer selection t study the tradeoff between efficiency, fairness and incentive in P2P streaming with SVC coding in general topology. We employ mesh-based designs to implement taxation in P2P t layered streaming. Single rate SVC/MDC p2p II. MODELING LAYERED P2P STREAMING p2p streaming streaming In this section, we develop analytical models to study the interplay between the efficiency, fairness and incentive in layered P2P streaming. Fig. 1. Social welfare vs. personal welfare. interplay between efficiency, fairness and incentive in lay- A. Maximizing Efficiency ered P2P streaming. The models enable us to numerically When peers are cooperative, they are willing to contribute investigate the impact of peering strategies and chunk their upload bandwidth without any incentive. The design scheduling polices on the fundamental trade-offs between objective of the system is to maximize the aggregate video the three. quality on all peers. With layered coding, the perceived video • We adopt taxation mechanism to adjust the balance quality on a peer is an increasing function of the number between the social welfare and individual peer welfare. of video layers received. PSNR (Peak Signal-to-Noise Ratio) We develop practical taxation-based P2P layered stream- is the standard objective metric to evaluate the quality of ing designs, including layer subscription strategy, chunk a compressed video and thus can be adopted as the utility scheduling policy, and mesh topology adaptation. function in layered video streaming. PSNR of a video coded • Through extensive trace driven simulations, we demon- at rate rc can be approximated by a logarithmic function strate that the proposed layered streaming designs can β log(rc) [20], where β is a constant related to the video effectively drive P2P streaming systems to the desired feature. This approximation is also valid in the SVC case [13]. operating points in a distributed fashion. Let rd be the total rate of received video layers on peer The remaining of the paper is organized as follows. We d. If the aggregate uploading capacity of the server and briefly go through related work in the following section. all peers is U, the aggregate receiving rate on all peers is Utility-maximization models and numerical studies are pre- PN naturally bounded by d=1 rd ≤ U. Since log(·) is a concave sented in Section II. Taxation-based layered streaming designs function, the aggregate utility can be maximized when all peers are presented in Section III. The performance of the proposed U receive video at the same rate, i.e., rd = N . For single-layer designs are evaluated in Section IV. The paper is concluded video streaming, it was shown in [21] that, if peers are fully in Section V. connected, a two-hop relay streaming can achieve this optimal rate. The solution for layered video streaming naturally follows A. Related Work if we let all peers subscribe to the same number of video layers U The majority of existing P2P live streaming systems ignore allowed by the rate N and deliver each layer to all peers the fairness requirement and focus solely on maximizing the using the two-hop relay scheme. However, it is unrealistic to social welfare. For instance, [5] studies how to fully utilize have fully connected mesh in a large-scale streaming system. available upload bandwidth so as to maximize the video rate For arbitrary streaming topology, the utility maximization in that can be streamed. SVC stream is also employed to improve layered streaming deserves more study. the social welfare further in face of peer churn and fluctuation 1) Network Coding Model: We consider a SVC system of available bandwidth. Recently, [16] proposes a 3-stage where the source server encodes a video stream into L layers scheduling mechanism to achieve high throughput and low with nested dependency. Layer l can be decoded if all the video quality jitter. Fairness issue is studied in [9], [17], [18]. layers below l are received. A peer can subscribe up to k, [17] proposes a service differentiated peer selection mecha- k ≤ L, layers. The server multicasts each layer to all peers nism for P2P media streaming systems. Higher contribution subscribed to it. There are L simultaneous multicast sessions, peers are offered more flexibility and choice in selecting peers, one for each layer, in the P2P overlay network.