Analysis and Optimization of Sparse Random Linear Network Coding for Reliable Multicast Services

Point-to-multipoint communications are expected to play a pivotal role in
next-generation networks. This paper refers to a cellular system transmitting
layered multicast services to a multicast group of users. Reliability of
communications is ensured via different Random Linear Network Coding (RLNC)
techniques. We deal with a fundamental problem: the computational complexity of
the RLNC decoder. The higher the number of decoding operations is, the more the
user's computational overhead grows and, consequently, the faster the battery
of mobile devices drains. By referring to several sparse RLNC techniques, and
without any assumption on the implementation of the RLNC decoder in use, we
provide an efficient way to characterize the performance of users targeted by
ultra-reliable layered multicast services. The proposed modeling allows to
efficiently derive the average number of coded packet transmissions needed to
recover one or more service layers. We design a convex resource allocation
framework that allows to minimize the complexity of the RLNC decoder by jointly
optimizing the transmission parameters and the sparsity of the code. The
designed optimization framework also ensures service guarantees to
predetermined fractions of users. Performance of the proposed optimization
framework is then investigated in a LTE-A eMBMS network multicasting H.264/SVC
video services.