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

Abstract

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. The performance of the proposed optimization framework is then investigated in a LTE-A eMBMS network multicasting H.264/SVC video services.

Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs

“…In turn, each RSU forwards to the FO and then to a cloud-based service the received coded packets. Hence, the cloud-based service can populate a K × n decoding matrix where the n columns of M are defined by the n columns in G associated with coded packets in C. The source message is recovered as soon as the rank of M becomes equal to K. In particular, the probability R(n) of a source message of being recovered, as a function of n, can be expressed follows [12]:…”

Random Linear Network Coding for 5G Mobile Video Delivery

“…However, even for P d (N), exact expressions are unknown but only approximated by upper/lower bounds [19,20]. Adaptive extension to S-RLNC is proposed in the form of Tunable Sparse RLNC (TS-RLNC) [21].…”

“…, L are the enhancement layers. Assuming our system model adopts a NOW-RLNC implementation, any resource allocation strategy has to answer the following questions [19,[69][70][71]:…”

“…We provide an overview of RLNC placed in the context of a packet-level data processing protocol sublayer that can be easily integrated at different layers of protocol stack within mobile video delivery environment. The RLNC sublayer can be further optimized and improved with respect to complexity and video quality using sparse and unequal error protection RLNC design [19][20][21][22]. In parallel with the review of RLNC, we provide an in-depth overview of mobile video delivery, focusing on unicast and multicast/broadcast mobile video services over fourth generation (4G) Long-Term Evolution (LTE) network [9,[23][24][25].…”

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

“…For example, Tassi et al [13] discussed a convex resource allocation framework that allows minimizing the complexity of RLNC. In some cases, decoding complexity is not the only issue of RLNC solutions, since the overhead caused by the coding vector that needs to be embedded in each coded packet shall be also considered.…”