ElasticVR：多用户多连接无线虚拟现实（VR）系统中的弹性任务计算
ElasticVR: Elastic Task Computing in Multi-User Multi-Connectivity Wireless Virtual Reality (VR) Systems
AI总结：
ElasticVR通过多智能体深度强化学习提升多用户多连接VR系统的弹性任务计算效率与性能
摘要：
多样化的新兴VR应用集成了需要大量计算和数据速率的高保真360视频内容流。可扩展的360视频分块能够实现弹性VR计算任务，这些任务可以根据可用的用户和系统资源进行适应性扩展。我们将可扩展的360视频分块整合到一个边缘-客户端无线多连接架构中，以实现跨多个VR用户的联合弹性任务计算卸载，称为ElasticVR。为了平衡通信、计算、能耗和QoE之间的权衡，我们提出了一个约束QoE和能耗优化问题，将多用户/多连接的动作空间与VR计算任务的弹性相结合。ElasticVR框架引入了两种多智能体深度强化学习解决方案，即CPPG和IPPG。CPPG采用集中训练和集中执行方法，以捕捉用户通信和计算需求之间的耦合。这导致了全局协调的决策，但以增加的计算开销和有限的可扩展性为代价。为了解决后者挑战，我们还探索了一种替代策略，称为IPPG，它采用集中训练与分布式执行范式。IPPG利用共享信息和参数共享来学习鲁棒策略；然而，在执行过程中，每个用户仅基于其本地状态信息独立采取行动。分布式执行减轻了集中决策的通信和计算开销，并提高了可扩展性。我们显示，ElasticVR框架相比不将弹性纳入VR计算的案例，将PSNR提高了43.21%，同时将响应时间和能耗分别降低了42.35%和56.83%。
英文摘要：
Diverse emerging VR applications integrate streaming of high fidelity 360 video content that requires ample amounts of computation and data rate. Scalable 360 video tiling enables having elastic VR computational tasks that can be scaled adaptively in computation and data rate based on the available user and system resources. We integrate scalable 360 video tiling in an edge-client wireless multi-connectivity architecture for joint elastic task computation offloading across multiple VR users called ElasticVR. To balance the trade-offs in communication, computation, energy consumption, and QoE that arise herein, we formulate a constrained QoE and energy optimization problem that integrates the multi-user/multi-connectivity action space with the elasticity of VR computational tasks. The ElasticVR framework introduces two multi-agent deep reinforcement learning solutions, namely CPPG and IPPG. CPPG adopts a centralized training and centralized execution approach to capture the coupling between users' communication and computational demands. This leads to globally coordinated decisions at the cost of increased computational overheads and limited scalability. To address the latter challenges, we also explore an alternative strategy denoted IPPG that adopts a centralized training with decentralized execution paradigm. IPPG leverages shared information and parameter sharing to learn robust policies; however, during execution, each user takes action independently based on its local state information only. The decentralized execution alleviates the communication and computation overhead of centralized decision-making and improves scalability. We show that the ElasticVR framework improves the PSNR by 43.21%, while reducing the response time and energy consumption by 42.35% and 56.83%, respectively, compared with a case where no elasticity is incorporated into VR computations.
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