  In this paper, channel optimized distributed multiple description vector
quantization (CDMD) schemes are presented for distributed source coding in
symmetric and asymmetric settings. The CDMD encoder is designed using a
deterministic annealing approach over noisy channels with packet loss. A
minimum mean squared error asymmetric CDMD decoder is proposed for effective
reconstruction of a source, utilizing the side information (SI) and its
corresponding received descriptions. The proposed iterative symmetric CDMD
decoder jointly reconstructs the symbols of multiple correlated sources. Two
types of 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 (in a wireless sensor network
setting), maximum mutual information and minimum end-to-end distortion. The
performance of the proposed systems and algorithms are evaluated and compared
in detail.
