Peer-to-Peer Streaming of Layered Video: Efficiency, Fairness and Incentive

Abstract

Abstract-Recent advance in scalable video coding (SVC) makes it possible for users to receive the same video with different qualities. To adopt SVC in P2P streaming, two key design questions need to be answered: 1) layer subscription: how many layers each peer should receive? 2) layer scheduling: how to deliver to peers the layers they subscribed? From the system point of view, the most efficient solution is to maximize the aggregate video quality on all peers, i.e., the social welfare. From individual peer point of view, the solution should be fair. Fairness in P2P streaming should additionally take into account peer contributions to make the solution incentive-compatible. In this paper, we first develop utility maximization models to understand the interplay between efficiency, fairness and incentive in layered P2P streaming. We show that taxation mechanisms can be devised to strike the right balance between social welfare and individual peer welfare. We then develop practical taxation-based P2P layered streaming designs, including layer subscription strategy, chunk scheduling policy, and mesh topology adaptation. Extensive trace-driven simulations show that the proposed designs can effectively drive layered P2P streaming systems to converge to the desired operating points in a distributed fashion.

Using topology information for quality‐aware Peer‐to‐Peer multilayer video streaming

“…In order to compare two generated states, equation (23) depicts the sum that expresses a solution into a numeric value, which adheres to our proposed objective strategy; the numeric value of the solution state is the sum of the benefit values b i according to the received video layers l i over all destination nodes d in the network topology. When the solution state value is optimized, the minimum received number of video layers at each destination node is maximized.…”

“…Parameter delta is the numeric difference between two proposals, for example, calculated by the sum in equation (23). New proposals that are slightly worse than the current state have a higher chance to be temporarily accepted.…”

“…Figure 13 introduces the process for accepting or rejecting a new solution state. First, the objective value is calculated on line C.02 by using the algorithm specified in equation (23). When the new solution state produces a better objective value, the proposed state is accepted instantly and the current objective value is adjusted (C.05), else the inferior solution state is accepted according to the Metropolis criterion of equation (24).…”