Rate control with pairwise intersession network coding

  • Authors:

  • Abdallah Khreishah;Chih-Chun Wang;Ness B. Shroff

  • Affiliations:

  • Center for Wireless Systems and Applications, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN;Center for Wireless Systems and Applications, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN;Departments of Electrical and Computer Engineering and Computer Science and Engineering, The Ohio State University, Columbus, OH

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2010

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Abstract

In this paper, we develop a distributed rate-control algorithm for networks with multiple unicast sessions when network coding is allowed across different sessions. Building on recent flow-based characterization of pairwise intersession network coding, the corresponding optimal rate-control problem is formulated as a convex optimization problem. The formulation exploits pairwise coding possibilities between any pair of sessions, where any coded symbol is formed by coding over at most two original symbols. The objective function is the sum of the utilities based on the rates supported by each unicast session. Working on the Lagrangian of the formulated problem, a distributed algorithm is developed with little coordination among intermediate nodes. Each unicast session has the freedom to choose its own utility function. The only information exchange required by the source is the weighted sum of the queue length of each link, which can be piggybacked to the acknowledgment messages. In addition to the optimal rate-control algorithm, we propose a decentralized pairwise random coding scheme that decouples the decision of coding from that of rate control, which further enhances the distributiveness of the proposed scheme. The convergence of the rate-control algorithm is proven analytically and verified by extensive simulations. Simulation results also demonstrate the advantage of the proposed algorithm over the state-of-the-art in terms of both throughput and fairness.