From Instantly Decodable to Random Linear Network Coding

Our primary goal in this paper is to traverse the performance gap between two linear network coding schemes: random linear network coding (RLNC) and instantly decodable network coding (IDNC) in terms of throughput and decoding delay. We first redefine the concept of packet generation and use it to partition a block of partially-received data packets in a novel way, based on the coding sets in an IDNC solution. By varying the generation size, we obtain a general coding framework which consists of a series of coding schemes, with RLNC and IDNC identified as two extreme cases. We then prove that the throughput and decoding delay performance of all coding schemes in this coding framework are bounded between the performance of RLNC and IDNC and hence throughput-delay tradeoff becomes possible. We also propose implementations of this coding framework to further improve its throughput and decoding delay performance, to manage feedback frequency and coding complexity, or to achieve in-block performance adaption. Extensive simulations are then provided to verify the performance of the proposed coding schemes and their implementations.Comment: 30 pages with double space, 14 color figure

From instantly decodable to random linear network coding

Our primary goal in this paper is to better understand and extend the achievable tradeoffs between the throughput and decoding delay performance of network coded wireless broadcast. To this end, we traverse the performance gap between two linear network coding schemes: random linear network coding (RLNC) and instantly decodable network coding (IDNC). Our approach is to appropriately partition a block of partially received data packets into subgenerations and broadcast them separately using RLNC. Through analyzing the factors that affect the performance of a generic partitioning scheme, we are led to develop a coding framework in which subgenerations are created from IDNC coding sets in an IDNC solution. This coding framework consists of a series of coding schemes, with classic RLNC and IDNC identified as two extreme schemes. We develop two basic partitioning guidelines, including disjoint partitioning and even partitioning. We design various implementations of this coding framework, such as partitioning algorithms and generation scheduling strategies, to further improve its throughput and decoding delay, to manage feedback frequency and coding complexity, or to achieve in-block performance adaption. Their effectiveness is verified through extensive simulations, and their performance is compared with an existing work in the literature

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