Learning-Augmented Streaming Codes are Approximately Optimal for Variable-Size Messages

TL;DR

This paper introduces a novel approach combining algebraic coding with learning-augmented algorithms to design streaming codes that are approximately rate-optimal for variable-size messages in real-time communication.

Contribution

It presents the first approximately rate-optimal streaming codes that adapt to variable message sizes using a learning-augmented approach, addressing a key challenge in online data transmission.

Findings

Achieves near-optimal data spreading in streaming codes.

Effectively handles variable message sizes in real-time.

Demonstrates practical applicability for streaming communication.

Abstract

Real-time streaming communication requires a high quality of service despite contending with packet loss. Streaming codes are a class of codes best suited for this setting. A key challenge for streaming codes is that they operate in an "online" setting in which the amount of data to be transmitted varies over time and is not known in advance. Mitigating the adverse effects of variability requires spreading the data that arrives at a time slot over multiple future packets, and the optimal strategy for spreading depends on the arrival pattern. Algebraic coding techniques alone are therefore insufficient for designing rate-optimal codes. We combine algebraic coding techniques with a learning-augmented algorithm for spreading to design the first approximately rate-optimal streaming codes for a range of parameter regimes that are important for practical applications.