Optimization of encoding configuration in scalable multiple description coding for rate-adaptive P2P video multicasting

Abstract

It is well-known that in peer-to-peer (P2P) streaming from a single source, single point of failure can be avoided by multiple description coding over multiple multicast trees since it provides path diversity. In this scenario, we propose using scalable multiple description coding (SMDC), where each description is scalable so that all descriptions can be efficiently adapted to the available rate of each link for effective congestion control. We also propose a multiple objective optimization (MOO) framework for selection of the best encoding configuration for SMDC from a set of candidates, which will strike the best balance between minimizing average end-to-end rate-distortion performance of each description given a set of packet loss probabilities, while minimizing overall redundancy and maximizing the range of extraction points of each scalable description. The optimization variables are some SVC encoding parameters and MD generation alternatives that result in different levels of redundancy at a fixed total rate for all descriptions. The framework can be used for optimization over other SVC encoding variables and MD generation methods if desired. Results of Monte-Carlo simulation of SMDC streaming of videos demonstrate the performance of the proposed method.