A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogeneity

TL;DR

This paper presents a mathematical framework and a novel mixed scheduling strategy, SchedMix, to improve swarming-based live streaming by effectively leveraging client heterogeneity, validated through analysis, simulations, and implementation.

Contribution

It introduces a new mathematical model for swarming on random graphs and proposes SchedMix, a mixed strategy that outperforms existing scheduling methods in heterogeneous client environments.

Findings

SchedMix outperforms LDF and EDF strategies in heterogeneous scenarios.

Mathematical analysis and simulations confirm SchedMix's effectiveness.

Full-stack implementation demonstrates practical viability.

Abstract

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 pure streaming approach or in combination with tree/push mechanisms. However, the impact of realistic client populations with heterogeneous resources is not yet fully understood. In this technical report, 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 main contributions are twofold: (1) a mathematical framework for swarming on random graphs is proposed with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) a mixed strategy, named SchedMix, is proposed that leverages peer heterogeneity. The proposed strategy, SchedMix is shown to outperform the other two strategies using different abstractions: a mean-field theoretic analysis of buffer probabilities, simulations of a stochastic model on random graphs, and a full-stack implementation of a P2P streaming system.