On-the-Fly Overlapping of Sparse Generations: A Tunable Sparse Network Coding Perspective

Abstract

Traditionally, the idea of overlapping generations in network coding research has focused on reducing the complexity of decoding large data files while maintaining the delay performance expected of a system that combines all data packets. However, the effort for encoding and decoding individual generations can still be quite high compared to other sparse coding approaches. This paper focuses on an inherently different approach that combines (i) sparsely coded generations configured on-the-fly based on (ii) controllable and infrequent feedback that allows the system to remove some original packets from the pool of packets to be mixed in the linear combinations. The latter is key to maintain a high impact of the coded packets received during the entire process while maintaining very sparsely coded generations. Interestingly, our proposed approach naturally bridges the idea of overlapping generations with that of tunable sparse network coding, thus providing the system with a seamless and adaptive strategy to balance complexity and delay performance. We analyze two families of strategies focused on these ideas. We also compare them to other standard approaches both in terms of delay performance and complexity as well as providing measurements in commercial devices to support our conclusions. Our results show that a judicious choice of the overlapping of the generations provides close-to-optimal delay performance, while reducing the decoding complexity by up to an order of magnitude with respect to other schemes.

Markov Chain Model for the Decoding Probability of Sparse Network Coding

“…Zhao et al proposed in [9] a slightly simplified modification, proposing a generalization of the original expression. However, the existing model that analytically characterize sparse coding scheme are far from being accurate or have some limitations, for instance the approximations proposed in [22], [28], [32]. In particular, the authors of [27], [28], [33] exploit a lower bound to find a trade-off between complexity and overhead; such lower bound establishes that the probability of receiving a new linearly independent packet by the decoder, when it already has i linearly independent packets is:…”

Performance and complexity of tunable sparse network coding with gradual growing tuning functions over wireless networks

“…Heide et al [14] analyzed the impact of different operational parameters (generation size, field size and density) over the RLNC overhead; their approach is similar to the one used in [6], combining a small number of packets (low density) in each transmission. Moreover, a more complex scheme where sparse codes and overlapping generations was exploited by by Sørensen et al in [15].…”

Random linear fountain code with improved decoding success probability

“…Sorensen et al have proposed to use overlapping generations to minimize the decoding complexity [9]. The general idea of performing encoding on overlapping generation is to divide the k input packets into smaller sub-generations, each sub-generations having smaller number of input packets.…”