An Evolutionary Approach To Inter-Session Network Coding

Abstract

Whereas the theory and application of optimal network coding are well studied for the single-session multicast scenario, there is no known optimal network coding strategy for a more general connection problem where there are more than one session and receivers may demand different sets of information. Though there have been a number of recent studies that demonstrate various utilities of network coding in the multisession scenario, they rely on very restricted classes of codes in terms of the coding operations allowed and/or the location of decoding. In this paper, we propose a novel inter-session network coding strategy for a general connection problem. Our coding strategy allows fairly general random linear coding over a large finite field, in which decoding is done at receivers and the mixture of information at interior nodes is controlled by evolutionary mechanisms. We demonstrate how our coding strategy may surpass existing end-to-end pairwise XOR coding schemes in terms of effectiveness and practicality.

Multicast packing for coding across multiple unicasts

“…An evolutionary approach is proposed in [8] for the same problem. Both approaches have to face the scalability problem.…”

“…Each iteration of the for-loop corresponds to a stage. The major body of the for-loop is a while-loop (lines [7][8][9][10][11][12][13][14][15][16][17][18][19]. At the beginning of the while-loop, the algorithm calls a function get to generate a new partition G 1 from G (line 8).…”

“…We used a network (see Fig. 4), which has been used by other researchers [8] [7], for our simulations. It has been shown by previous work [8] [7] that network coding exhibits better performance than routing only when shared bottlenecks are present.…”

“…For example, the integer linear programming (ILP) approach [7] is difficult to analyze since it needs to consider all possible butterfly structures in the network. On the other hand, the evolutionary approach [8], does not have a mathematical formulation. Third, in order to find the best MPC, we only need to search the space of all partitions of the set of unicast flows, independently of the network size.…”

“…For example, the approach in [7] uses integer linear programming to select the best set of butterflies considering all pairs of flows, but also all possible coding points on the network topology. The evolutionary approach [8] involves a random walk in the space of local coding vectors, which does not scales well with the network size. Although independent of the network size, our search problem is still exponential in the number of unicast flows.…”