Cliff effect suppression through multiple-descriptions with split personality

摘要

We propose a compression/transmission scheme that allows the quality of the reconstructed signal to gracefully degrade as the channel quality drops, as well as steadily improve with the channel improvement. The main idea is to partition the channel and/or network resources into m units (e.g., sub-bands, packets) and compress the source independently m times to perfectly match single unit resources, thus creating m independently distorted source versions. Consequently, we create a multiple-description, joint source-channel like architecture, that enables efficient reconstruction starting from a single received description with improvements onward. We further split the compression rate in two parts, allocating one to a rate-distortion optimal encoder, and the other to transmitting uncoded source symbols. We show how this architecture can easily leverage modularity in terms of adjustable rate-splitting ratio and the maximum number of descriptions, e.g., through software parameters, to simultaneously and robustly (i.e. avoiding the cliff effect) achieve operating points close to rate-distortion curve for many channel states. We demonstrate how statistical description of channel states (or performance statistics of content delivery network) can be used to set the two parameters constructively in terms of converging to optimal operation in the range of interest.