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.