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

Andrea Tassi, Ioannis Chatzigeorgiou, Daniel Enrique Lucani Rötter

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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.

Original languageEnglish
Article number7335581
JournalIEEE Transactions on Communications
Volume46
Issue1
Pages (from-to)285-299
Number of pages15
ISSN0090-6778
DOIs
Publication statusPublished - 2016
Externally publishedYes

Keywords

  • EMBMS
  • Green communications
  • LTE-A
  • Mobile communication
  • Multicast communication
  • Resource allocation
  • Sparse network coding
  • Ultra-reliable communications

Fingerprint

Dive into the research topics of 'Analysis and Optimization of Sparse Random Linear Network Coding for Reliable Multicast Services'. Together they form a unique fingerprint.

Cite this