Paper Info
Title
Localized P2P VoD Delivery Scheme with Pre-Fetching for Broadband Access Networks.
Abstract
Video on Demand (VoD) service has become an increasingly popular service in recent years as a result of the rapid deployment of Fiber-to-the-Home networks. Due to its enormous bandwidth and stringent quality of service requirements, deploying an efficient and scalable VoD service still remains a challenge. In this paper, we propose a peer-to peer (P2P) VoD distribution scheme for PONs in which the P2P delivery is localized to within the same access network and in which a selected set of movies is pre-fetched into the customer premises equipment during off-peak hours. The proposed delivery scheme mitigates the load from the VoD server by exploiting the participation of the customer equipment in VoD distribution. In turn, this optimizes bandwidth consumption of the VoD service in both core and metro networks as the P2P video traffic is localized within the access network. We formulate movie pre-fetching as an optimization problem which determines the number of copies of each movie to be pre-fetched, and we propose a heuristic algorithm to solve it. Using simulations we show that our proposed replication algorithm performs much better than existing popularity based replication algorithm that has been proposed for similar purposes. Moreover, we show that our proposed delivery scheme effectively reduced the server load in busy hours while having high but random server load reduction in off-peak hours of service.
Year
DOI
Venue
2011
10.1109/GLOCOM.2011.6133940
IEEE Global Telecommunications Conference (Globecom)
Keywords
Field
DocType
IPTV,Peer-to-Peer,Pre-fetching,Passive optical networks,Video-on-Demand,Zipf
Customer-premises equipment,Computer science,Heuristic (computer science),Passive optical network,Server,Computer network,Quality of service,Real-time computing,Broadband networks,Access network,Scalability
Conference
ISSN
Citations 
PageRank 
1930-529X
4
0.44
References 
Authors
8
4
Name
Order
Citations
PageRank
Chamil Jayasundara1143.75
Ampalavanapillai Nirmalathas24916.48
Elaine Wong3163.86
Chien Aun Chan4186.52