Joint Source-Channel Coding at the Application Layer for Parallel Gaussian Sources

TL;DR

This paper proposes a layered joint source-channel coding scheme for multicasting parallel Gaussian sources over erasure channels, optimizing performance via convex rate-distortion functions and embedded scalar quantizers.

Contribution

It introduces a novel layered coding approach combining embedded quantizers with rateless encoders for efficient multicasting over erasure channels.

Findings

Optimized layered coding schemes for Gaussian sources.

Convex optimization based on rate-distortion functions.

Effective multicasting over binary erasure channels.

Abstract

In this paper the multicasting of independent parallel Gaussian sources over a binary erasure broadcasted channel is considered. Multiresolution embedded quantizer and layered joint source-channel coding schemes are used in order to serve simultaneously several users at different channel capacities. The convex nature of the rate-distortion function, computed by means of reverse water-filling, allows us to solve relevant convex optimization problems corresponding to different performance criteria. Then, layered joint source-channel codes are constructed based on the concatenation of embedded scalar quantizers with binary rateless encoders.