Performance and Complexity of Tunable Sparse Network Coding with Gradual Growing Tuning Functions over Wireless Networks

Random Linear Network Coding (RLNC) has been
shown to be a technique with several benefits, in particular when
applied over wireless mesh networks, since it provides robustness
against packet losses. On the other hand, Tunable Sparse
Network Coding (TSNC) is a promising concept, which leverages
a trade-off between computational complexity and goodput. An
optimal density tuning function has not been found yet, due to the
lack of a closed-form expression that links density, performance
and computational cost. In addition, it would be difficult to
implement, due to the feedback delay. In this work we propose
two novel tuning functions with a lower computational cost,
which do not increase the overhead in terms of the transmission
of linear dependent packets compared with Random Linear
Network Coding (RLNC) and previous proposals. Furthermore,
we also broaden previous studies of TSNC techniques, by means
of an extensive simulation campaign carried out using the
ns-3 simulator. This brings the possibility of assessing their
performance over more realistic scenarios, e.g considering MAC
effects and delays. We exploit this implementation to analyze
the impact of the feedback sent by the decoder. The results
show a reduction of 3.5× in the number of operations without
jeopardizing the network performance, in terms of goodput, even
when we consider the delay effect on the feedback sent by the
decoder