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Joint User Association and Power Allocation for Minimizing Multi-bitrate Video Transmission Delay in Mobile-Edge Computing Networks

机译:用于最小化移动边缘计算网络中的多比特率视频传输延迟的联合用户关联和功率分配

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Fast-growing video services place higher demands on network performance especially in terms of latency, but the traditional networks architecture with congested backhaul link can no longer meet the requirement. Recently, mobile edge computing(MEC)has become a promising paradigm to achieve low latency performance and can provide multi-bitrate video streaming at the edge of radio access networks(RAN)with the ability of caching and transcoding. In this paper, we consider the scenario of multi-cell MEC networks, where each BS deployed with one MEC server is connected to the core network through the limited-capacity backhaul link. Our goal is to minimize the system delay which includes backhaul transmission delay and wireless side transmission delay. To this end, we propose a collaborative optimization of user-BS association and power allocation strategy with the given cache status. This is a mixed-integer nonlinear programming(MINLP)problem which is NP-hard. Thus we propose an improved genetic algorithm to solve this problem based on the traditional genetic algorithm. Simulation results demonstrate that our proposed algorithm performs better in terms of convergence and can get better solution as compared with traditional genetic algorithm.
机译:快速增长的视频服务在延迟方面,对网络性能的需求较高,但具有拥塞回程链路的传统网络架构不再满足要求。最近,移动边缘计算(MEC)已成为实现低延迟性能的有希望的范例,并且可以在无线电接入网络(RAN)的边缘提供多比特率视频流,具有缓存和转码的能力。在本文中,我们考虑了多电池MEC网络的场景,其中通过一个MEC服务器部署的每个BS通过有限容量的回程链路连接到核心网络。我们的目标是最小化包括回程传输延迟和无线侧传输延迟的系统延迟。为此,我们提出了具有给定高速缓存状态的User-BS关联和电力分配策略的协同优化。这是一个混合整数非线性编程(MINLP)问题,它是NP-HARD。因此,我们提出了一种改进的遗传算法,基于传统的遗传算法来解决这个问题。仿真结果表明,与传统遗传算法相比,我们所提出的算法在收敛方面表现更好,并且可以获得更好的解决方案。

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