Analysing and leveraging client heterogeneity in swarming-based live streaming

Due to missing IP multicast support on an Internet scale, over-the-top media streams are delivered with the help of overlays as used by content delivery networks and their peer-to-peer (P2P) extensions. In this context, mesh/pull-based swarming plays an important role either as a pure streaming approach or in combination with tree/push mechanisms. The crucial impact of today's variety of client systems with their heterogeneous resources is not yet well understood. In this paper, we contribute to closing this gap by mathematically analysing the most basic scheduling mechanisms latest deadline first (LDF) and earliest deadline first (EDF) in a continuous time Markov chain framework and combining them into a simple, yet powerful, mixed strategy to leverage inherent differences in client resources. The contribution of this paper is, hence, twofold: (1) we develop a mathematical framework for swarming on random graphs with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) we propose a mixed strategy, named SchedMix, that leverages client heterogeneity. We show that SchedMix outperforms LDF and EDF using different abstractions: a mean-field theoretic analysis of buffer probabilities, simulations of the stochastic model on random graphs, and a full-stack implementation of a P2P streaming system.