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HySAC: A hybrid delivery system with adaptive content management for IPTV networks

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2011 IEEE International Conference on Communications, ICC 2011
DOI - pysyväislinkit
TilaJulkaistu - 2011
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma2011 IEEE International Conference on Communications, ICC 2011 - Kyoto, Japani
Kesto: 5 kesäkuuta 20119 kesäkuuta 2011

Conference

Conference2011 IEEE International Conference on Communications, ICC 2011
MaaJapani
KaupunkiKyoto
Ajanjakso5/06/119/06/11

Tiivistelmä

The emergence of new platforms providing Internet protocol television (IPTV) services, such as Google TV [1] and Apple TV [2] is expected to revolutionize the way multimedia services are provided on the Internet. However, the existing network infrastructure falls well short of meeting the resource requirements to provide satisfactory levels of such services. This has led the service providers like Google to contemplate deploying large farms of network cables to transport the traffic generated by their IPTV services. However, the huge cost involved in achieving their target, means careful resource planning and meeting users' expectations are essential. In this paper, we propose an effective adaptive HYbrid delivery System Adaptive Content management (HySAC), which dynamically selects the mode of video delivery from unicast, multicast and peer-to-peer (P2P) based on the popularity of the respective videos. The videos are dynamically classified into high, medium and low popularity groups and, they are delivered using multicast, P2P and unicast methods respectively. HySAC reduces the start-up delay as the mode of delivery is well defined and the resource availability to support the delivery is guaranteed. We have evaluated the performance of HySAC on a realistic simulated environment and presented experimental results; they indicate that HySAC performs significantly better than pure unicast, multicast and P2P mechanisms. This paper also provides a mechanism to classify the videos into different popularity classes.