סמינר: Graduate Seminar
Broadcast Approach Meets Network Coding for Ultra-Reliable and Low-Latency Data Streaming
Date:
October,28,2024
Start Time:
16:00 - 17:00
Location:
1061, Meyer Building
Zoom:
Zoom link
Add to:
Lecturer:
Ofek Cohen
Research Areas:
| 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., variable recovered information data at the receiver over variable short block length and rate is proposed. 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 posterior as the enhancement layer, which contains the remaining data to augment the streaming service's quality, is with the relaxed delay constraints.
We provide numerical evidence that the layered network coding strategy significantly boosts performance. Specifically, our results indicate that LAC-RLNC, when compared to the traditional non-layered method, achieves up to a twofold reduction in the mean and max in-order delivery delay for the base layer, nearing the theoretical lower limit, while maintaining similar results in the enhancement layer, and a slight improvement in throughput. These findings are corroborated by our analytical work, which produces bounds aligning with simulation data and facilitates the management of the throughput-delay trade-off. M.Sc. student under the supervision of Prof. Alejandro Cohen.
|