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.