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Quality-optimized downlink scheduling for video streaming applications in LTE networks

机译:LTE网络中视频流应用的质量优化的下行链路调度

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As the next generation of all-IP mobile communication system, LTE offers unprecedented data transmission speed and low latency for a variety of applications and services. However efficient QoS provisioning for wireless networks is challenging due to unreliable and resource-constrained radio interface. In this paper, we investigate the important downlink scheduling problem in LTE networks with a focus on video streaming applications. Unlike the conventional scheduling rules which exploit the network layer metrics, our scheme is directly targeted on optimizing the application-layer video quality within the required end-to-end delay bound. The video quality optimized scheduling is formulated as a complex combinatorial optimization problem with an exponentially-growing search space. To solve this complex optimization problem, we exploit the GA (genetic algorithm) based metaheuristic approach. The intrinsic strength of population based solution of GA offers a superior advantage for this type of scheduling problems. Performance of the proposed crosslayer design and the GA solution is evaluated against the well-known M-LWDF scheduling rule and a trajectory method. The simulation results demonstrate the effectiveness of the GA based quality-optimized approach. It can enhance the video quality significantly and satisfy the delay bound.
机译:作为下一代全IP移动通信系统,LTE为各种应用和服务提供了空前的数据传输速度和低延迟。然而,由于不可靠且资源受限的无线电接口,用于无线网络的有效QoS设置具有挑战性。在本文中,我们将重点研究视频流应用,研究LTE网络中重要的下行链路调度问题。与利用网络层指标的常规调度规则不同,我们的方案直接针对在所需的端到端延迟范围内优化应用层视频质量。视频质量优化的调度表述为搜索空间呈指数增长的复杂组合优化问题。为了解决这个复杂的优化问题,我们利用了基于遗传算法的元启发式方法。基于种群的遗传算法解决方案的内在优势为此类调度问题提供了优越的优势。针对著名的M-LWDF调度规则和轨迹方法,评估了建议的跨层设计和GA解决方案的性能。仿真结果证明了基于遗传算法的质量优化方法的有效性。它可以显着提高视频质量并满足延迟范围。

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