Stir: Spontaneous social peer-to-peer streaming

Dealing with a high churn rate is very challenging in live peer-to-peer streaming. State-of-the-art studies try to mitigate the problem by exploiting peer dynamic models, analyzing traces from real world systems, or using enhanced coding techniques, e.g., network coding. Applications of social networking in peer-to-peer systems, especially on file sharing, have recently received research attention. In such systems, the establishment of connections among peers is based on social relationships among users, which are, however, not formed in the context of a peer-to-peer session but, e.g., imported from other social networks. Since friends in such a separate social network do not always have similar interests, they may not necessarily join or stay long in the same peer-to-peer session. We believe that a tight integration between the high level social network of users and the low level overlay of peers would bring significant benefits in dealing with high churn rates and providing personalized streaming services. This paper presents Stir, the first attempt towards an integrated social peer-to-peer streaming system. The key feature of Stir is that social relationships among users are spontaneously formed in a streaming session, and can be exploited directly by the underlying streaming protocol. Stir users, who join the same session, can make friends by means of spontaneous communication, e.g., instant messaging. Such social network formation provides a reliable indication to deal with high churn rate. Our simulations with real social data and peer dynamic traces have demonstrated the benefits of Stir and shed light on building such a system in practice.