On the multicast capacity in energy-constrained lossy wireless networks by exploiting intrabatch and interbatch Network Coding

Peng Li, Song Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


We study a fundamental problem in determining the multicast capacity in energy-constrained wireless networks with lossy transmission links. The multicast capacity in our paper is defined as the maximum number of packets that can be disseminated from the source and successfully received by all multicast destinations. To explore the expected multicast capacity, we propose a framework for the joint optimization of both dynamic power control and error control. In our framework, the lossy wireless transmission links are characterized by the Rayleigh fading model, which reveals the realistic relationship among link quality, transmission power, and path attenuation. Under this model, we exploit the reliability gain of random linear network coding, also referred to as intrabatch coding in this paper, by disseminating data in batches. To maximize multicast capacity, another type of network coding opportunities across batches, referred to as interbatch coding, is also explored. Our analytical framework based on intrabatch and interbatch network coding eventually leads to a linear programming formulation that is proved to obtain the optimal multicast capacity. To approach the theoretical results in practice, we propose an algorithm called DMCC that exploits the intrabatch and interbatch coding via dynamically constructing bottleneck trees. Extensive simulations are conducted to show that its performance is very close to the optimal solution.
Original languageEnglish
Article number6365181
Pages (from-to)2251-2260
Number of pages10
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number11
Publication statusPublished - 14 Oct 2013
Externally publishedYes


  • energy efficiency
  • Multicast capacity
  • network coding
  • reliability

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

Cite this