Analysis and Optimization of Sparse Random Linear Network Coding for Reliable Multicast Services

TL;DR

This paper analyzes the computational complexity of sparse RLNC in multicast services, proposing an optimization framework to reduce decoding complexity while ensuring service reliability in LTE-A networks.

Contribution

It introduces a novel modeling approach for sparse RLNC performance and a convex optimization framework to minimize decoder complexity with service guarantees.

Findings

Efficient characterization of user decoding performance.

Optimized transmission parameters reduce decoding complexity.

Framework ensures reliability for targeted user fractions.

Abstract

Point-to-multipoint communications are expected to play a pivotal role in next-generation networks. This paper refers to a cellular system transmitting layered multicast services to a multicast group of users. Reliability of communications is ensured via different Random Linear Network Coding (RLNC) techniques. We deal with a fundamental problem: the computational complexity of the RLNC decoder. The higher the number of decoding operations is, the more the user's computational overhead grows and, consequently, the faster the battery of mobile devices drains. By referring to several sparse RLNC techniques, and without any assumption on the implementation of the RLNC decoder in use, we provide an efficient way to characterize the performance of users targeted by ultra-reliable layered multicast services. The proposed modeling allows to efficiently derive the average number of coded packet transmissions needed to recover one or more service layers. We design a convex resource allocation framework that allows to minimize the complexity of the RLNC decoder by jointly optimizing the transmission parameters and the sparsity of the code. The designed optimization framework also ensures service guarantees to predetermined fractions of users. The performance of the proposed optimization framework is then investigated in a LTE-A eMBMS network multicasting H.264/SVC video services.