A systematic framework for dynamically optimizing delay-sensitive wireless transmission

Maryam Karimi, M. Dehghan, Seyyed Majid Nourhoseini
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Abstract

Delay sensitive applications need to overcome the service problems in dynamic environments with respect to both the multimedia source data (e.g., variable bit-rate) and the wireless channels (e.g., fading channel). This paper considers the problem of point to point transmission of scalable video coding over a fading channel. We formulate the rate adaptation challenge of WLAN multimedia networks as a Markov Decision Process and resolve this problem online based on reinforcement learning. The buffer state, channel state, and video state were considered as a joint state of system to maximize the average Quality of Service under delay constraints. To improve the convergence speed of learning, system's underlying dynamics were partitioned into a priori known and a priori unknown components. The proposed learning algorithm exploits known information about the system, so that less information needs to be learned compared with that in conventional reinforcement learning algorithms.
动态优化延迟敏感无线传输的系统框架
延迟敏感型应用需要克服动态环境中多媒体源数据(如可变比特率)和无线信道(如衰落信道)的服务问题。研究了衰落信道中可扩展视频编码的点对点传输问题。我们将无线局域网多媒体网络的速率自适应挑战表述为一个马尔可夫决策过程,并基于强化学习在线解决该问题。将缓冲状态、信道状态和视频状态作为系统的联合状态,在时延约束下使平均服务质量最大化。为了提高学习的收敛速度,将系统的底层动力学划分为先验已知和先验未知两部分。该学习算法利用了系统的已知信息,与传统的强化学习算法相比,需要学习的信息更少。
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