ABSTRACT
Video sharing has been an increasingly
popular application in online social networks (OSNs). However, its sustainable development
is severely hindered by the intrinsic limit of the client/server architecture
deployed in current OSN video systems, which is not only costly in terms of
server bandwidth and storage but also not scalable with the soaring amount of
users and video content. The peer-assisted Video-on-Demand (VoD) technique, in
which participating peers assist the server in delivering video content has
been proposed recently. Unfortunately, videos can only be disseminated through
friends in OSNs. Therefore, current VoD works that explore clustering nodes
with similar interests or close location for high performance are suboptimal,
if not entirely inapplicable, in OSNs. Based on our long-term real-world
measurement of over 1,000,000 users and 2,500 videos on Facebook, we propose
SocialTube, a novel peer-assisted video sharing system that explores social
relationship, interest similarity, and physical location between peers in OSNs.
Specifically, SocialTube incorporates four algorithms: a social network
(SN)-based P2P overlay construction algorithm, a SN-based chunk prefetching
algorithm, chunk delivery and scheduling algorithm, and a buffer management
algorithm. Experimental results from a prototype on PlanetLab and an
event-driven simulator show that SocialTube can improve the quality of user
experience and system scalability over current P2P VoD techniques.
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