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For data streaming applications, existing solutions are not yet able to closethe gap between high data rates and low delay. This work considers the problemof data streaming under mixed delay constraints over a single communicationchannel with delayed feedback. We propose a novel layered adaptive causalrandom linear network coding (LAC-RLNC) approach with forward error correction.LAC-RLNC is a variable-to-variable coding scheme, i.e., variable recoveredinformation data at the receiver over variable short block length and rate isproposed. Specifically, for data streaming with base and enhancement layers ofcontent, we characterize a high dimensional throughput-delay trade-off managedby the adaptive causal layering coding scheme. The base layer is designed tosatisfy the strict delay constraints, as it contains the data needed to allowthe streaming service. Then, the sender can manage the throughput-delaytrade-off of the second layer by adjusting the retransmission rate a priori andposterior as the enhancement layer, that contains the remaining data to augmentthe streaming service's quality, is with the relax delay constraints. Wenumerically show that the layered network coding approach can dramaticallyincrease performance. We demonstrate that LAC-RLNC compared with thenon-layered approach gains a factor of three in mean and maximum delay for thebase layer, close to the lower bound, and factor two for the enhancement layer.

Broadcast Approach Meets Network Coding for Data Streaming