Wireless virtual reality (VR) is increasingly used in industrial Internet of Things (IIoTs). However, ultra-high viewport rendering demands and excessive terminal energy consumption restrict the application of wireless VR. Pervasive edge computing emerges as a promising method for wireless VR. In this article, we propose an energy-aware resource management scheme for wireless-VR-supported IIoTs. To reduce the energy consumption of VR equipments (VEs) while ensuring a smooth immersive VR experience, we formulate the viewport rendering offloading, computing, and spectrum resource allocation to be a joint optimization problem, considering content correlation between VEs, fluctuating channel conditions, and VR quality of experience. By applying dual approximation, the original problem is transformed to be a Markov decision process and an reinforcement learning (RL)-based online learning algorithm is designed to find the optimal policy. To improve the learning efficiency, the quantum parallelism is integrated into the RL to overcome “curse of dimensionality”. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. Simulation results demonstrate the effectiveness of the proposed scheme.

中文翻译：

---

工业物联网中普适边缘计算辅助无线 VR 流媒体的资源管理

无线虚拟现实 (VR) 越来越多地用于工业物联网 (IIoT)。然而，超高的视口渲染需求和过多的终端能耗限制了无线VR的应用。无处不在的边缘计算成为无线 VR 的一种有前途的方法。在本文中，我们为支持无线 VR 的 IIoT 提出了一种能量感知资源管理方案。为了在保证沉浸式 VR 体验流畅的同时降低 VR 设备（VE）的能耗，我们将视口渲染卸载、计算和频谱资源分配制定为一个联合优化问题，考虑 VE 之间的内容相关性、​​波动的信道条件和VR体验质量。通过应用对偶近似，将原始问题转化为马尔可夫决策过程，并设计基于强化学习 (RL) 的在线学习算法来寻找最优策略。为了提高学习效率，将量子并行性集成到 RL 中以克服“维数灾难”。在模拟中，评估收敛速度和能量消耗和停转率方面的性能。仿真结果证明了所提出方案的有效性。