Multimedia Systems

, Volume 15, Issue 1, pp 19–32 | Cite as

Towards capacity and profit optimization of video-on-demand services in a peer-assisted IPTV platform

  • Yih-Farn Chen
  • Yennun Huang
  • Rittwik Jana
  • Hongbo Jiang
  • Michael Rabinovich
  • Jeremy Rahe
  • Bin Wei
  • Zhen Xiao
Regular Paper

Abstract

This paper studies the conditions under which peer-to-peer (P2P) technology may be beneficial in providing IPTV services over typical network architectures. It has three major contributions. First, we contrast two network models used to study the performance of such a system: a commonly used logical “Internet as a cloud” model and a “physical” model that reflects the characteristics of the underlying network. Specifically, we show that the cloud model overlooks important architectural aspects of the network and may drastically overstate the benefits of P2P technology. Second, we propose an algorithm called Zebra that pre-stripes content across multiple peers during idle hours to speed up P2P content delivery in an IPTV environment with limited upload bandwidth. We also perform simulations to measure Zebra’s effectiveness at reducing load on the content server during peak hours. Third, we provide a cost-benefit analysis of P2P video content delivery, focusing on the profit trade-offs for different pricing/incentive models rather than purely on capacity maximization. In particular, we find that under high volume of video demand, a P2P built-in incentive model performs better than any other model, while the conventional no-P2P model generates more profits when the request rate is low. The flat-reward model generally falls in between the usage-based model and the built-in model in terms of profitability except for low request rates. We also find that built-in and flat-reward models are more profitable than the usage-based model for a wide range of subscriber community sizes.

Keywords

IPTV P2P streaming Content distribution network FTTN Video-on-demand 

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Yih-Farn Chen
    • 1
  • Yennun Huang
    • 2
  • Rittwik Jana
    • 1
  • Hongbo Jiang
    • 3
  • Michael Rabinovich
    • 3
  • Jeremy Rahe
    • 4
  • Bin Wei
    • 1
  • Zhen Xiao
    • 5
  1. 1.AT&T Shannon Research LaboratoryFlorham ParkUSA
  2. 2.Institute for Information IndustryTaipeiTaiwan
  3. 3.Case Western Reserve UniversityClevelandUSA
  4. 4.U.C. BerkeleyBerkeleyUSA
  5. 5.Peking UniversityBeijingChina

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