IEEE 802.11 wlan中分层视频多播的最佳带宽适应

Yen-Ting Chen, Hung-Yi Teng, Ren-Hung Hwang, Jeng-Farn Lee
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引用次数: 1

摘要

随着无线局域网(wlan)的广泛采用,视频电话、视频游戏和移动电视广播等无线链路上的视频传播已经成为快速增长的应用。由于无线信道容易出错,需要合理选择移动站的接收数据速率,以避免丢包率高、业务质量严重下降。此外,为了提高视频组播的可靠性,应用层前向纠错(AL-FEC)是一种很有前途的防止丢包的方法。然而,在无线局域网接入点(AP)上,如何自适应地选择合适的传输速率和AL-FEC码率组合来传输无线局域网中的多播分层视频成为一个重要问题。本文提出了一种基于奖励的带宽自适应机制,以动态确定IEEE 802.11 wlan中组播分层视频的传输速率和AL-FEC码率的最佳组合。我们的目标是在给定的资源预算下,最大限度地提高系统奖励,这代表了所有用户体验到的视频质量。提出了一种基于奖励的策略迭代算法作为求解优化问题的启发式方法,并将其性能与穷举搜索和遗传算法两种基准算法进行了比较。我们通过仿真验证了该方法的性能。仿真结果表明,该方法在系统资源稀疏的情况下仍能提供较好的服务质量。
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Optimal bandwidth adaptation for layered video multicasting in IEEE 802.11 WLANs
With widespread adoption of wireless local area networks (WLANs), video dissemination over wireless links such as video telephony, video gaming, and mobile TV broadcasting have become fast growing applications. Since wireless channel is error-prone, the receiving data rate of mobile stations (MSs) needs to be selected properly to avoid high packet loss rate and serious service quality degradation. In addition, to enhance reliability of video multicasting, application-layer forward error correction (AL-FEC) is a promising approach against packet losses. However, at the WLAN access point (AP), how to adaptively select a proper combination of transmission rate and AL-FEC code rate to multicast layered videos in WLANs becomes an important issue. In this paper, we propose a reward-based bandwidth adaptation mechanism to dynamically determine the best combination of transmission rate and AL-FEC code rate to multicast layered video in IEEE 802.11 WLANs. Our goal is to maximize the system reward which represents the video quality experienced on all users, under a given resource budget. A reward-based policy iteration algorithm is proposed as a heuristic approach to solve the optimization problem and its performance is compared with that of two baseline algorithms: exhaustive search and genetic algorithm. We demonstrate the performance of our approach via simulations. The simulation results show our approach can provide better service quality even through system resource is sparse.
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