Adaptive topology formation for peer-to-peer video streaming

In this paper we propose an adaptive P2P video streaming framework to address the challenges due to bandwidth heterogeneity and peer churn on the Internet. This adaptive streaming framework consists of two major components, source rate adaptation and adaptive overlay topology formation, to maximize the video quality and fully utilize the overall peer upload capacity. In the source rate adaptation, the video server adapts the video source rate automatically based on the local measurement of peers’ download rates, so that the P2P network is not overloaded beyond its bandwidth Parts of the results in this paper have been presented in the IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, Nevada, USA, Jan. 2008. This work is supported by RGC Earmarked Research Grant 620306. H. Luan Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada e-mail: [email protected] K.-W. Kwong Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA e-mail: [email protected] X. Hei (B) Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China, 430074 e-mail: [email protected] D. H. K. Tsang Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China e-mail: [email protected] capacity and peers are able to achieve smooth video playback. To combat bandwidth heterogeneity, we propose to construct a desirable link-level homogeneous overlay topology using a Markov chain Monte Carlo method, so that peers achieve an equal per-connection upload/download bandwidth. In this link-level homogeneous network, video flows do not encounter any bottlenecks along the delivery paths, and peers achieve high download rates to ensure smooth video playback. We also design a fully distributed algorithm to implement the dual mechanisms of the adaptive topology formation and the source rate maximization. To evaluate the performance of our streaming framework, we conduct both mathematical analysis and extensive simulations. The simulation results confirm our analysis and show that the proposed distributed algorithm is able to maximize the video playback quality with fast convergence.