Video sharing has been an increasingly popular application in online social networks (OSNs). Be that as it may, its reasonable development is severely hindered by the intrinsic limit of the client/server architecture development in current OSN video systems, which isn’t costly in terms of server bandwidth and capacity yet in addition not scalable with the taking off measure of clients 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, recordings must be scattered through friends in OSNs. In this way, current VoD works that investigate clustering nodes with comparative premiums or close area for superior are problematic, if not by any means inapplicable, in OSNs. In light of our longterm certifiable estimation of more than 1,000,000 clients and 2,500 videos on Facebook, we propose SocialTube, a novel peer-assisted video sharing system that explores a social relationship, interest similarity, and a physical area between peers in OSNs. In particular, SocialTube incorporates four algorithms: a Social Network (SN)- based P2P overlay construction algorithm, an SN-based chunk prefetching algorithm, chunk delivery and scheduling algorithm, and a buffer management algorithm. Experimental results about because of a prototype on PlanetLab and an event-driven simulator demonstrate that SocialTube can enhance the quality of client experience and system scalability over current P2P VoD techniques.