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 characterise the performance of users targeted by ultra-reliable layered multicast services. The proposed modelling 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 minimise the complexity of the RLNC decoder by jointly optimising the transmission parameters and the sparsity of the code. The designed optimisation framework also ensures service guarantees to predetermined fractions of users. The performance of the proposed optimisation framework is then investigated in a LTE-A eMBMS network multicasting H.264/SVC video services

Resource Tuned Optimal Random Network Coding for Single Hop Multicast future 5G Networks

Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix

Resource Tuned Optimal Random Network Coding for Single Hop Multicast future 5G Networks

Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

Network-coded NOMA with antenna selection for the support of two heterogeneous groups of users

The combination of Non-Orthogonal Multiple Access (NOMA) and Transmit Antenna Selection (TAS) techniques has recently attracted significant attention due to the low cost, low complexity and high diversity gains. Meanwhile, Random Linear Coding (RLC) is considered to be a promising technique for achieving high reliability and low latency in multicast communications. In this paper, we consider a downlink system with a multi-antenna base station and two multicast groups of single-antenna users, where one group can afford to be served opportunistically, while the other group consists of comparatively low power devices with limited processing capabilities that have strict Quality of Service (QoS) requirements. In order to boost reliability and satisfy the QoS requirements of the multicast groups, we propose a cross-layer framework including NOMAbased TAS at the physical layer and RLC at the application layer. In particular, two low complexity TAS protocols for NOMA are studied in order to exploit the diversity gain and meet the QoS requirements. In addition, RLC analysis aims to facilitate heterogeneous users, such that, sliding window based sparse RLC is employed for computational restricted users, and conventional RLC is considered for others. Theoretical expressions that characterize the performance of the proposed framework are derived and verified through simulation results

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research

Throughput and Delay Optimization of Linear Network Coding in Wireless Broadcast

Linear network coding (LNC) is able to achieve the optimal throughput of packet-level wireless broadcast, where a sender wishes to broadcast a set of data packets to a set of receivers within its transmission range through lossy wireless links. But the price is a large delay in the recovery of individual data packets due to network decoding, which may undermine all the benefits of LNC. However, packet decoding delay minimization and its relation to throughput maximization have not been well understood in the network coding literature. Motivated by this fact, in this thesis we present a comprehensive study on the joint optimization of throughput and average packet decoding delay (APDD) for LNC in wireless broadcast. To this end, we reveal the fundamental performance limits of LNC and study the performance of three major classes of LNC techniques, including instantly decodable network coding (IDNC), generation-based LNC, and throughput-optimal LNC (including random linear network coding (RLNC)). Various approaches are taken to accomplish the study, including 1) deriving performance bounds, 2) establishing and modelling optimization problems, 3) studying the hardness of the optimization problems and their approximation, 4) developing new optimal and heuristic techniques that take into account practical concerns such as receiver feedback frequency and computational complexity. Key contributions of this thesis include: - a necessary and sufficient condition for LNC to achieve the optimal throughput of wireless broadcast; - the NP-hardness of APDD minimization; - lower bounds of the expected APDD of LNC under random packet erasures; - the APDD-approximation ratio of throughput-optimal LNC, which has a value of between 4/3 and 2. In particular, the ratio of RLNC is exactly 2; - a novel throughput-optimal, APDD-approximation, and implementation-friendly LNC technique; - an optimal implementation of strict IDNC that is robust to packet erasures; - a novel generation-based LNC technique that generalizes some of the existing LNC techniques and enables tunable throughput-delay tradeoffs