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Zebroid: using IPTV data to support peer-assisted VoD content delivery
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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 II table of contents
Pages 115-120  
Year of Publication: 2009
ISBN:978-1-60558-433-1
Authors
Yih-Farn Robin Chen  AT&T Labs - Research, Florham Park, NJ, USA
Rittwik Jana  AT&T Labs - Research, Florham Park, NJ, USA
Daniel Stern  AT&T Labs - Research, Florham Park, NJ, USA
Bin Wei  AT&T Labs - Research, Florham Park, NJ, USA
Mike Yang  AT&T Labs - Research, Florham Park, NJ, USA
Hailong Sun  Beihang University, Beijing, China
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

P2P file transfers and streaming have already seen a tremendous growth in Internet applications. With the rapid growth of IPTV, the need to efficiently disseminate large volumes of Video-on-Demand (VoD) content has prompted IPTV service providers to consider peer-assisted VoD content delivery. This paper describes Zebroid, a VoD solution that uses IPTV operational data on an on-going basis to determine how to pre-position popular contents in customer set-top boxes during idle hours to allow these peers to assist the VoD server in content delivery during peak hours. Latest VoD request distribution, set-top box availability, and capacity data on network components are all taken into consideration in determining the parameters used in the striping algorithm of Zebroid. We show both by simulation and emulation on a realistic IPTV testbed that the VoD server load can be significantly reduced by more than 50-80% during peak hours by using Zebroid.


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:
Yih-Farn Robin Chen: colleagues
Rittwik Jana: colleagues
Daniel Stern: colleagues
Bin Wei: colleagues
Mike Yang: colleagues
Hailong Sun: colleagues