Resource-Aware Joint Source-Channel Multiple Description Estimation and Compression in Networks

We present a joint source-channel multiple description (JSC-MD) framework for resource-constrained network communications (e.g., sensor networks), in which one or many deprived encoders communicate a Markov source against bit errors and erasure errors to many decoders, some powerful and some deprived. To keep the encoder complexity at minimum, the source is coded into $K$ descriptions by a simple multiple description quantizer (MDQ) with neither entropy nor channel coding. The code diversity of MDQ and the path diversity of the network are exploited by decoders to correct transmission errors and improve coding efficiency. To suit heterogeneous decoders the proposed JSC-MD approach is made resource-scalable. Powerful nodes in the network can perform JSC-MD distributed estimation/decoding under the criteria of maximum A posteriori probability (MAP) or minimum mean-square error (MMSE), while primitive nodes resort to simpler MD decoding, all working with the same MDQ code. The application of JSC-MD to distributed estimation of hidden Markov models in a sensor network is demonstrated. The proposed JSC-MD MMSE decoder is an extension of the well-known forward-backward algorithm to multiple descriptions, while the JSC-MD MAP decoder is an algorithm of the longest path in a weighted directed acyclic graph. Both algorithms simultaneously exploit the source memory, the redundancy of the fixed-rate MDQ, and the inter-description correlations. They outperform the existing hard-decision MDQ decoders by large margins (up to 8dB). The new JSC-MD framework also enjoys an operational advantage over the existing MDQ decoders. It eliminates the need for as many as $2^{K}-1$ side decoders to handle different combinations of the received descriptions by unifying the treatments of all these possible cases.