From Instantly Decodable to Random Linear Network Coded Broadcast

Abstract

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.

Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks

“…2) a received coded packet is either instantly decoded using the past decoded packets or discarded (i.e., it is not stored for later decoding). Because of these properties, IDNCs have been the subject of several work studying real time multimedia broadcast [9], [10], [24]. Some existing works such as [25] and [26] relax the second property, and allow a receiver to temporary store non-instantly decodable packets (instead of discarding them) and utilize them later in the decoding process.…”

“…They further extend their results to general network topologies. Yu et al [10] analyze the tradeoff between throughput and decoding delay, and examine the performance gap between RLNC and IDNC. They also propose a number of coding solutions with varying delaythroughput tradeoffs.…”

Hardware Acceleration for RLNC: A Case Study Based on the Xtensa Processor with the Tensilica Instruction-Set Extension

“…Aside from the computation delay for RLNC encoding and decoding, the latency introduced by RLNC depends on the employed network coding approach. There are two main approaches for network coding for low-latency communications: coding based only on Exclusive OR (XOR) operations, see, e.g., [36][37][38][39][40][41][42], and coding based on feedback from the receivers to the sender, see,e.g., [43][44][45][46][47][48]. The XOR approach corresponds to network coding for binary Galois fields, GF (2).…”

Network Coding-Based Information Sharing Strategy for Reducing Energy Consumption in IoT Environments

“…(broadcast) 환경에서 네트워크 부호 기반 효과적인 재전송에 대한 연구 [7][8][9][10][11] , 네트워크 상의 노드들이 네 트워크 부호화를 통해 서로 정보를 효과적으로 교환 하는 연구 [12,13] , 그리고 네트워크 부호화 기법을 사용 한 시스템의 에너지 소모에 관한 연구 [14,15] …”