Leveraging users' likes in a video streaming P2P platform

This paper investigates how a p2p television platform can take advantage of the presence of frequent channel viewers to grant them a more satisfying service than to less regular spectators. The idea we explore is to learn beforehand about the users' interests, in order to cluster them in groups that display different behaviors; then, the neighborhood creation strategy and video chunk scheduling algorithm of the overlay is altered, with the aim of serving frequent spectators in a privileged manner, providing them with a faster access to the selected channel without overly penalizing less habitual customers. An analytical model is developed, to capture the difference in startup delay that the proposed modifications introduce; several additional performance metrics are numerically determined, in order to thoroughly size up the performance of both groups of viewers. The obtained results show that a clear service differentiation is achieved and also quantify the effects that alternative neighborhood creation algorithms have on the amount of such gap.