MEC辅助的VR视频业务中能量-延迟权衡的混合策略学习
Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service
摘要：
虚拟现实（VR）有望从根本上改变广泛的行业领域以及人类与虚拟内容交互的方式。然而，尽管取得了前所未有的进步，但目前的网络和计算基础设施无法释放VR的全部潜力。在本文中，我们考虑通过移动边缘计算（MEC）网络提供无线多瓦片VR视频服务。主要目标是最小化系统延迟/能量消耗并达到其折衷。为此，我们首先将时变视图流行度转换为无模型马尔可夫链，以有效地捕获其动态特性。在联合评估MEC服务器和VR播放设备的缓存和计算能力之后，然后实施混合策略以协调动态缓存替换和确定性卸载，从而充分利用系统资源。底层的多目标问题被重新表述为一个部分可观测的马尔可夫决策过程，并提出了一个深度确定性的策略梯度算法迭代学习其解决方案，其中一个长短期记忆神经网络被嵌入到连续预测的动态不可观测的流行。仿真结果表明，所提出的方案在实现能源效率和延迟减少之间的权衡优于基线方法。
英文摘要：
Virtual reality (VR) is promising to fundamentally transform a broad spectrum of industry sectors and the way humans interact with virtual content. However, despite unprecedented progress, current networking and computing infrastructures are incompetent to unlock VR's full potential. In this paper, we consider delivering the wireless multi-tile VR video service over a mobile edge computing (MEC) network. The primary goal is to minimize the system latency/energy consumption and to arrive at a tradeoff thereof. To this end, we first cast the time-varying view popularity as a model-free Markov chain to effectively capture its dynamic characteristics. After jointly assessing the caching and computing capacities on both the MEC server and the VR playback device, a hybrid policy is then implemented to coordinate the dynamic caching replacement and the deterministic offloading, so as to fully utilize the system resources. The underlying multi-objective problem is reformulated as a partially observable Markov decision process, and a deep deterministic policy gradient algorithm is proposed to iteratively learn its solution, where a long short-term memory neural network is embedded to continuously predict the dynamics of the unobservable popularity. Simulation results demonstrate the superiority of the proposed scheme in achieving a trade-off between the energy efficiency and the latency reduction over the baseline methods.
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