Multimedia Systems

, Volume 16, Issue 3, pp 199–214 | Cite as

Zebroid: using IPTV data to support STB-assisted VoD content delivery

  • Yih-Farn Robin Chen
  • Rittwik Jana
  • Daniel Stern
  • Bin Wei
  • Mike Yang
  • Hailong Sun
  • Jagadeesh Dyaberi
Regular Paper

Abstract

IPTV, unlike Internet TV, delivers digital TV and multimedia services over IP-based networks with the required level of quality of service (QoS) and quality of experience (QoE). Linear programming channels in IPTV are delivered through multicast, which is highly scalable with the number of subscribers. Video-on-demand (VoD) content, on the other hand, is typically delivered using unicast, which places a heavy load on the VoD servers and all the network components leading to the end-user set-top boxes (STBs) as the demand increases. With the rapid growth of IPTV subscribers and the shift in video viewing habits, the need to efficiently disseminate large volumes of VoD content has prompted IPTV service providers to consider the use of STBs to assist in video content delivery. This paper describes our current research work on Zebroid, a potential VoD solution for fiber-to-the-node (FTTN) networks, which uses IPTV data on a recurring basis to determine how to select, stripe, and preposition popular content in selected STBs during idle hours. A STB requesting VoD content during the peak hours can then receive necessary stripes from participating STBs in the neighborhood. Recent VoD request access patterns, STB availability data, and capacity data on network components are 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 reduced by more than 70% during peak hours by allocating only 8 GB of storage on each STB. The savings achieved through Zebroid would also allow IPTV service providers to add more linear programming channels without expensive infrastructure upgrades.

Keywords

Video-on-demand Peer-to-peer IPTV 

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Yih-Farn Robin Chen
    • 1
  • Rittwik Jana
    • 1
  • Daniel Stern
    • 1
  • Bin Wei
    • 1
  • Mike Yang
    • 1
  • Hailong Sun
    • 2
  • Jagadeesh Dyaberi
    • 3
  1. 1.AT&T Labs ResearchFlorham ParkUSA
  2. 2.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  3. 3.Purdue UniversityWest LafayetteUSA

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