Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems

TL;DR

This paper proposes a decentralized, adaptive helper selection algorithm for multi-channel P2P streaming that ensures convergence to equilibria, balancing load and maintaining streaming quality despite dynamic network conditions.

Contribution

It introduces a distributed online helper selection mechanism that guarantees convergence to correlated equilibria using regret-tracking, addressing helper cooperation in P2P streaming.

Findings

Achieves good convergence to equilibria

Balances load distribution among helpers

Maintains sustainable streaming rates

Abstract

In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus bandwidth resources act as micro-servers to compensate the server deficiencies in balancing the resources between different channel overlays. With deployment of helper level between server and peers, optimizing the user/helper topology becomes a challenging task since applying well-known reciprocity-based choking algorithms is impossible due to the one-directional nature of video streaming from helpers to users. Because of selfish behavior of peers and lack of central authority among them, selection of helpers requires coordination. In this paper, we design a distributed online helper selection mechanism which is adaptable to supply and demand pattern of various video channels. Our solution for strategic peers' exploitation from the shared resources of helpers is to guarantee the convergence to correlated equilibria (CE) among the helper selection strategies. Online convergence to the set of CE is achieved through the regret-tracking algorithm which tracks the equilibrium in the presence of stochastic dynamics of helpers' bandwidth. The resulting CE can help us select proper cooperation policies. Simulation results demonstrate that our algorithm achieves good convergence, load distribution on helpers and sustainable streaming rates for peers.