Home > Research > Publications & Outputs > Analysis and optimization of sparse random line...

Electronic data

Links

Text available via DOI:

View graph of relations

Analysis and optimization of sparse random linear network coding for reliable multicast services

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>01/2016
<mark>Journal</mark>IEEE Transactions on Communications
Issue number1
Volume64
Number of pages15
Pages (from-to)285-299
Publication statusPublished
Early online date23/11/15
Original languageEnglish

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 characterise the performance of users targeted by ultra-reliable layered multicast services. The proposed modelling 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 minimise the complexity of the RLNC decoder by jointly optimising the transmission parameters and the sparsity of the code. The designed optimisation framework also ensures service guarantees to predetermined fractions of users. The performance of the proposed optimisation framework is then investigated in a LTE-A eMBMS network multicasting H.264/SVC video services.

Bibliographic note

©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.