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InstantLeap: fast neighbor discovery in P2P VoD streaming
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International Workshop on Network and Operating System Support for Digital Audio and Video archive
Proceedings of the 18th international workshop on Network and operating systems support for digital audio and video table of contents
Williamsburg, VA, USA
SESSION: Peer-to-peer streaming I table of contents
Pages 19-24  
Year of Publication: 2009
ISBN:978-1-60558-433-1
Authors
Xuanjia Qiu  Sun Yat-Sen University, Guangzhou, China
Chuan Wu  The University of Hong Kong, Hong Kong, Hong Kong
Xiaola Lin  Sun Yat-Sen University, Guangzhou, China
Francis C.M. Lau  The University of Hong Kong, Hong Kong, Hong Kong
Sponsors
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

A fundamental challenge in peer-to-peer (P2P) Video-on-Demand (VoD) streaming is to quickly locate new supplying peers whenever a VCR command is issued, in order to achieve smooth viewing experiences. For most existing commercial systems which resort to tracking servers for such neighbor discovery, the increasing scale of P2P VoD systems has brought heavy load onto the dedicated servers. To avoid overloading the servers and achieve instant neighbor discovery over the self-organizing P2P overlay, we design a novel method of organizing peers watching the same video, that constitutes a light-weighted indexing structure to support efficient streaming and fast neighbor discovery at the same time. InstantLeap achieves an O(1) neighbor discovery efficiency upon any playback "leaps" across the media stream in streaming overlays of any sizes, with a low messaging cost for the overlay maintenance. We support our design with rigorous analysis and extensive simulations.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Xuanjia Qiu: colleagues
Chuan Wu: colleagues
Xiaola Lin: colleagues
Francis C.M. Lau: colleagues