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.

中文翻译：

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MEC 辅助 VR 视频服务中能量延迟权衡的混合策略学习


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，当前的网络和计算基础设施仍不足以释放 VR 的全部潜力。在本文中，我们考虑通过移动边缘计算（MEC）网络提供无线多块 VR 视频服务。主要目标是最小化系统延迟/能耗并达到其折衷。为此，我们首先将时变视图流行度投射为无模型马尔可夫链，以有效捕获其动态特征。联合评估MEC服务器和VR播放设备上的缓存和计算能力后，实施混合策略来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部分可观察的马尔可夫决策过程，并提出了一种深度确定性策略梯度算法来迭代学习其解决方案，其中嵌入了长短期记忆神经网络来连续预测不可观察的动态受欢迎程度。仿真结果表明，与基线方法相比，所提出的方案在实现能源效率和延迟减少之间的权衡方面具有优越性。