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July  2011, 7(3): 699-716. doi: 10.3934/jimo.2011.7.699

## Queueing analysis of data block synchronization mechanism in peer-to-peer based video streaming system

 1 Graduate School of Informatics, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto 606-8501, Japan 2 Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501

Received  September 2010 Revised  May 2011 Published  June 2011

In a Peer-to-Peer (P2P) based video streaming system such as Coolstreaming, a single video stream is decomposed into multiple sub-streams. A client-peer node receives the sub-streams from multiple parent-peer nodes, combining them into the original video stream. Each client-peer node has a synchronization buffer and a cache buffer. Data blocks are stored in the synchronization buffer in a sub-stream basis, and then forwarded into the cache buffer according to their sequence numbers. In this buffering system, data-block synchronization plays a crucial role to guarantee video quality. In this paper, we consider the performance of data-block synchronization scheme with which data blocks are simultaneously forwarded just after all the data blocks composing a macro data block arrive at the synchronization buffer. We model the synchronization buffer as a multiple-buffer queueing system with homogeneous Poisson arrival processes, deriving the mean forwarding interval. We also consider the frame loss probability for multiple-path video streaming, investigating how the number of sub-streams decreases the frame loss probability. Numerical examples show that increasing the number of sub-streams makes the average forwarding interval large, while the frame loss probability at the bottleneck router is improved. It is also shown that increasing the synchronization buffer decreases the average forwarding interval.
Citation: Sho Nanao, Hiroyuki Masuyama, Shoji Kasahara, Yutaka Takahashi. Queueing analysis of data block synchronization mechanism in peer-to-peer based video streaming system. Journal of Industrial & Management Optimization, 2011, 7 (3) : 699-716. doi: 10.3934/jimo.2011.7.699
##### References:
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show all references

##### References:
 [1] M. Castro, P. Druschel, A.-M. Kermarrec, A. Nandi, A. Rowstron and A. Singh, "SplitStream: High-Bandwidth Content Distribution in Cooperative Environments,", Proceedings of SOSP, (2003), 292.   Google Scholar [2] D. Jurca, J. Chakareski, J.-P. Wagner and P. Frossard, Enabling adaptive video streaming in P2P systems,, IEEE Communications Magazine, 45 (2007), 108.  doi: 10.1109/MCOM.2007.374427.  Google Scholar [3] K. Kirihara, H. Masuyama, S. Kasahara and Y. Takahashi, "Performance Analysis of a Decentralized Content Delivery System with FEC Recovery,", Advances in Queueing Theory and Network Applications, (2009), 265.   Google Scholar [4] B. Li, S. Xie, G. Y. Keung and X. Zhang, Coolstreaming: Design, theory, and practice,, IEEE Transactions on Multimedia, 9 (2007), 1661.  doi: 10.1109/TMM.2007.907469.  Google Scholar [5] B. Li, S. Xie, Y. Qu, G. Y. Keung, C. Lin, J. Liu and X. Zhang, "Inside the New Coolstreaming: Principles, Measurements and Performance Implications,", Proceedings of IEEE INFOCOM, (2008), 1031.  doi: 10.1109/INFOCOM.2008.157.  Google Scholar [6] B. Li and H. Yin, Peer-to-peer live video streaming on the internet: Issues, existing approaches, and challenges,, IEEE Communications Magazine, 45 (2007), 94.  doi: 10.1109/MCOM.2007.374425.  Google Scholar [7] V. N. Padmanabhan, H. J. Wang, P. A. Chou and K. Sripanidkulchai, "Distributing Streaming Media Content Using Cooperative Networking,", Proceedings of NOSSDAV, (2002), 177.   Google Scholar [8] , PPlive,, \url{http://www.pplive.com}., ().   Google Scholar [9] , Sopcast,, \url{http://www.sopcast.org}., ().   Google Scholar
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