Broadcast Approach Meets Network Coding for Ultra-Reliable and Low-Latency Data Streaming

For data streaming applications, existing solutions are not yet able to close the gap between high data rates and low delay. This work considers the problem of data streaming under mixed delay constraints over a single communication channel with delayed feedback. We propose a novel layered adaptive causal random linear network coding (LAC-RLNC) approach with forward error correction. LAC-RLNC is a variable-to-variable coding scheme, i.e., a variable amount of recovered information at the receiver over variable short block length and rate. Specifically, for data streaming with base and enhancement layers of content, we characterize a high-dimensional throughput-delay trade-off managed by the adaptive causal layering coding scheme. The base layer is designed to satisfy the strict delay constraints, as it contains the data needed to allow the streaming service. Then, the sender can manage the throughput-delay trade-off of the second layer by adjusting the retransmission rate a-priori and a-posteriori since the enhancement layer, which contains the remaining data to augment the streaming service’s quality, operates under relaxed delay constraints. We provide numerical evidence that the layered network coding strategy significantly boosts performance. Specifically, our results show that LAC-RLNC achieves up to a twofold reduction in mean delay for the base layer compared to the non-layered method, up to 3.5× compared to SR-ARQ, nearing the theoretical lower limit, while maintaining comparable enhancement layer delay and slightly improving throughput. These findings are corroborated by our analytical work, which produces bounds that closely align with simulation results and facilitates the management of the throughput-delay trade-off.