Universal network coding-based opportunistic routing for unicast

Abdallah Khreishah, Issa Khalil, Jie Wu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Network coding-based opportunistic routing has emerged as an elegant way to optimize the capacity of lossy wireless multihop networks by reducing the amount of required feedback messages. Most of the works on network coding-based opportunistic routing in the literature assume that the links are independent. This assumption has been invalidated by the recent empirical studies that showed that the correlation among the links can be arbitrary. In this work, we show that the performance of network coding-based opportunistic routing is greatly impacted by the correlation among the links. We formulate the problem of maximizing the throughput while achieving fairness under arbitrary channel conditions, and we identify the structure of its optimal solution. As is typical in the literature, the optimal solution requires a large amount of immediate feedback messages, which is unrealistic. We propose the idea of performing network coding on the feedback messages and show that if the intermediate node waits until receiving only one feedback message from each next-hop node, the optimal level of network coding redundancy can be computed in a distributed manner. The coded feedback messages require a small amount of overhead, as they can be integrated with the packets. Our approach is also oblivious to losses and correlations among the links, as it optimizes the performance without the explicit knowledge of these two factors.

Original languageEnglish
Article number6812230
Pages (from-to)1765-1774
Number of pages10
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number6
Publication statusPublished - Jun 1 2015
Externally publishedYes


  • Network coding
  • coded feedback
  • cross-layer design
  • feedback
  • link correlation
  • wireless networks

ASJC Scopus subject areas

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


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