Embedded cross-decoding scheme for multiple description based distributed source coding

Using multiple description (MD) coding mechanisms, this paper proposes a novel coding framework for error-resilience in distributed source coding (DSC) in sensor networks. In particular, scalable source descriptions are first generated using a symmetric scalable MD scalar quantizer. These descriptions are then layered Wyner-Ziv (WZ) coded using low-density parity-check accumulate (LDPCA) -based syndrome binning. The decoder consists of two side decoders which attempt to iteratively decode their respective description at various LDPCA puncturing rates in the presence of a correlated side information. A central decoder exploits the inter-description correlation to further enhance the WZ rate-distortion performance when both descriptions are partially or fully received. In contrast to earlier work, our proposed decoding scheme also exploits the correlation that exists between bit-planes. Experimental simulations reveal that, for a Gaussian source, the proposed system yields a performance improvement of roughly 0.66 dB when compared to not exploiting inter-description correlations.