Joint source-channel decoding of multiple description quantized Markov sequences

This paper proposes a framework for joint source-channel decoding of Markov sequences that are coded by a fixed-rate multiple description quantizer (MDQ), and transmitted via a lossy network. This framework is suited for lossy networks of primitive energy-deprived source encoders. Our technical approach is one of maximum a posteriori probability (MAP) sequence estimation that exploits both the source memory and the correlation between different MDQ descriptions. We solve the MAP estimation problem by computing the longest path in a weighted directed acyclic graph, at a complexity of O(L²NK), where N is the number of source symbols in the input sequence, K is the number of MDQ descriptions, and L is the number of codewords of the central quantizer. If the source sequence is Gaussian Markovian, the decoder complexity can be reduced to O(LNK). For MDQ-compressed Markov sequences impaired by both bit errors and erasure errors, the performance of joint source-channel MAP decoder can be 6 dB higher than the conventional hard-decision decoder. Furthermore, the new MDQ decoding technique unifies the treatments of different subsets of the K descriptions available at the decoder, circumventing the thorny issue of requiring up to 2^K - 1 MDQ side decoders.