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Energy-Efficient Adaptive Transmission of Scalable Video Streaming in Cognitive Radio Communications

机译:认知无线电通信中可伸缩视频流的节能自适应传输

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摘要

Cognitive radio (CR) is a promising technology to alleviate spectrum shortage and satisfy the huge demand of bandwidth for multimedia streaming in future mobile computing systems. The inherent features of CR pose tough challenges in provisioning quality of service (QoS) for acceptable user experience and minimizing energy consumption for multimedia transmissions. In this paper, scalable video coding and transmission rate adaptation are jointly considered in an energy-efficient scheme for transmissions of streaming media over CR with QoS guarantee. An event-driven discrete-time Markov control process model is introduced to formulate the QoS-guaranteed energy-efficient transmission problem as a constrained stochastic optimization problem. Based on estimations of potentials and the difference between performance measurement and QoS requirement, an online policy iteration algorithm is proposed to optimize energy consumption under QoS constraints directly. By exploiting the system dynamics, this algorithm does not depend on any prior knowledge of channel availability or fading statistics, and it can converge to a near optimum with a low computational burden. Simulation results demonstrate the effectiveness of the proposed method.
机译:认知无线电(CR)是一种有前途的技术,可以缓解频谱短缺并满足未来移动计算系统中多媒体流的巨大带宽需求。 CR的固有功能在提供服务质量(QoS)以提供可接受的用户体验以及将多媒体传输的能耗降至最低方面提出了严峻的挑战。在本文中,可伸缩视频编码和传输速率自适应是在节能方案中结合QoS保证在CR上传输流媒体的同时考虑的。引入事件驱动的离散时间马尔可夫控制过程模型,将QoS保证的节能传输问题表述为约束随机优化问题。基于对势能的估计以及性能度量与QoS要求之间的差异,提出了一种在线策略迭代算法来直接优化QoS约束下的能耗。通过利用系统动力学,该算法不依赖于任何有关信道可用性或衰落统计的先验知识,并且可以收敛到接近最佳的状态,而计算负担却很小。仿真结果证明了该方法的有效性。

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