An interest-aware video streaming platform: Shaping its architecture to better suit users' demands

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 video chunk scheduling algorithm of the p2p overlay is altered, with the aim of serving frequent spectators in a privileged manner, providing them with a faster access to the selected channel. An analytical model is developed, to capture the difference in startup delay that the proposed changes introduce; several additional performance metrics are numerically determined, in order to thoroughly size up the performance of different groups of viewers. The obtained results show that a clear service differentiation is achieved and also quantify the effects that the setting of different system parameters have on the amount of such gap.