Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems

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

Improving Streaming Capacity in Multi-Channel P2P VoD Systems via Intra-Channel and Cross-Channel Resource Allocation

Multi-channel Peer-to-Peer (P2P) Video-on-Demand (VoD) systems can be categorized into independent-channel P2P VoD systems and correlated-channel P2P VoD systems. Streaming capacity for a channel is defined as the maximal streaming rate that can be received by every user of the channel. In this paper, we study the streaming capacity problem in multi-channel P2P VoD systems. In an independent-channel P2P VoD system, there is no resource correlation among channels. Therefore, we can find the average streaming capacity for the independent-channel P2P VoD system by finding the streaming capacity for each individual channel, respectively. We propose a distributed algorithm to solve the streaming capacity problem for a single channel in an independent-channel P2P VoD system. The average streaming capacity for a correlated-channel P2P VoD system depends on both the intra-channel and cross-channel resource allocation. To better utilize the cross-channel resources, we first optimize the server upload allocation among channels to maximize the average streaming capacity and then propose cross-channel helpers to enable cross-channel sharing of peer upload bandwidths. We demonstrate in the simulations that the correlated-channel P2P VoD systems with both intra-channel and cross-channel resource allocation can obtain a higher average streaming capacity compared to the independent-channel P2P VoD systems with only intra-channel resource allocation