An adaptive weighted online AL-FEC algorithm over mobile multicast networks

C. Bouras, N. Kanakis
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引用次数: 4

Abstract

A crucial point on the delivery of multicast content over mobile multicast networks is the utilization of Forward Error Correction (FEC) codes on the application layer (AL-FEC) so as to introduce robustness against arbitrary erasures on different recipients without the need of inefficient common error control methods based on data retransmission. FEC is a feedback free error recovery method where the sender introduces redundant data in advance with the source data enabling the recipients to recover from different arbitrary packet losses. The main issue on the efficient application of AL-FEC protection is the adaptation of the introduced redundancy to the current network conditions, in order to avoid network resources wastage ensuring at the same time sufficient transmission robustness. Based on this, in this work we present an adaptive weighted online algorithm aiming at the efficient application of RaptorQ AL-FEC codes over mobile multicast services. The proposed algorithm adapts the introduced AL-FEC transmission overhead exploiting at first the reception reports ability defined by several mobile multicast standards and thereafter the performance properties of the newly introduced RaptorQ FEC codes. We introduce the competitive framework of the efficient application of AL-FEC protection over mobile multicast networks, in the context of which, we design and analyze the adaptive online weighted algorithm and we further provide a series of simulation results to analyze the performance of the proposed scheme.
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移动组播网络中自适应加权在线AL-FEC算法
在移动组播网络上传输组播内容的一个关键点是在应用层(AL-FEC)上使用前向纠错(Forward Error Correction, FEC)码,从而在不需要基于数据重传的低效通用错误控制方法的情况下,引入对不同接收方任意擦除的鲁棒性。FEC是一种无反馈的错误恢复方法,发送方事先在源数据中引入冗余数据,使接收方能够从不同的任意数据包丢失中恢复。AL-FEC保护有效应用的主要问题是引入的冗余对当前网络状况的适应性,以避免网络资源的浪费,同时保证足够的传输鲁棒性。在此基础上,本文提出了一种自适应加权在线算法,旨在将RaptorQ AL-FEC码有效地应用于移动组播业务。该算法首先利用几种移动组播标准定义的接收报告能力,然后利用新引入的RaptorQ FEC码的性能特性来适应引入的AL-FEC传输开销。介绍了AL-FEC保护在移动组播网络中高效应用的竞争框架,在此框架下,设计并分析了自适应在线加权算法,并提供了一系列仿真结果来分析所提方案的性能。
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