Channel Optimized Distributed Multiple Description Coding

In this paper, for robust and efficient transmission of multiple correlated sources over noisy channels with packet loss, a channel optimized distributed multiple description vector quantization (CDMD) scheme is presented. The proposed CDMD scheme enjoys low-complexity encoding and delay and a scalable CDMD decoder, which jointly reconstructs the symbols of an arbitrary number of correlated sources. This, for example, suits data-gathering applications in wireless sensor networks. The CDMD encoder is designed using a deterministic annealing approach based on a minimum mean squared error asymmetric CDMD. A CDMD decoder for asymmetric distributed source coding is presented, which takes into account the side information, as well as channel noise and packet loss. Two types of iterative symmetric CDMD decoders, namely the estimated-SI and the soft-SI decoders, are presented, which respectively exploit the reconstructed symbols and a posteriori probabilities of other sources as SI in iterations. In a multiple-source CDMD setting, for reconstruction of a source, three methods are proposed to select another source as its SI during the decoding. The methods operate based on minimum physical distance, maximum mutual information, and minimum end-to-end distortion. The performance of the proposed systems and algorithms are evaluated and compared in detail.