Achievable Moderate Deviations Asymptotics for Streaming Compression of Correlated Sources

TL;DR

This paper investigates the moderate deviations asymptotics for streaming compression of correlated sources, showing that delay can significantly improve error decay rates near Slepian-Wolf region boundaries.

Contribution

It establishes that for streaming Slepian-Wolf coding, delay enhances the moderate deviations constant near corner points, extending previous non-streaming results and generalizing to different delay requirements.

Findings

Delay improves error decay rates by a factor of T near Slepian-Wolf corners.

The results apply to both lossless and variable delay source coding.

Analytical tools from streaming information theory are adapted for the proofs.

Abstract

Motivated by streaming multi-view video coding and wireless sensor networks, we consider the problem of blockwise streaming compression of a pair of correlated sources, which we term streaming Slepian-Wolf coding. We study the moderate deviations regime in which the rate pairs of a sequence of codes converge, along a straight line, to various points on the boundary of the Slepian-Wolf region at a speed slower than the inverse square root of the blocklength $n$, while the error probability decays subexponentially fast in $n$. Our main result focuses on directions of approaches to corner points of the Slepian-Wolf region. It states that for each correlated source and all corner points, there exists a non-empty subset of directions of approaches such that the moderate deviations constant (the constant of proportionality for the subexponential decay of the error probability) is enhanced (over the non-streaming case) by at least a factor of $T$, the block delay of decoding source block pairs. We specialize our main result to the setting of streaming lossless source coding and generalize this result to the setting where we have different delay requirements for each of the two source blocks. The proof of our main result involves the use of various analytical tools and amalgamates several ideas from the recent information-theoretic streaming literature. We adapt the so-called truncated memory encoding idea from Draper and Khisti (2011) and Lee, Tan, and Khisti (2016) to ensure that the effect of error accumulation is nullified in the limit of large blocklengths. We also adapt the use of the so-called minimum weighted empirical suffix entropy decoder which was used by Draper, Chang, and Sahai (2014) to derive achievable error exponents for symbolwise streaming Slepian-Wolf coding.