The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer for graphics rendering and augments weak graphics processing unit capacity of stand-alone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency: Longer latency requires rendering a larger viewport and streaming more content. In this article, we aim to utilize multi-access edge computing to assist the backend cloud in such remote rendered interactive VR. Given the dependency between latency and amount and quality of the content streamed, our objective is to jointly optimize the tradeoff between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming, we leverage the interpolation between client’s field of view frame size and overall latency to convert the problem to integer nonlinear programming model and then design efficient online algorithms to solve it. The results of our simulations supplemented by real-world user data reveal that enabling a desired balance between video quality and latency, our algorithm particularly achieves the improvements of on average about 22% and 12% in term of video delivery latency and 8% in term of video quality compared to respectively order-of-arrival, threshold-based, and random-location strategies.

中文翻译：

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多层 CloudVR

5G 移动网络中高带宽和低延迟通信的可用性支持远程渲染实时虚拟现实 (VR) 应用程序。云中 VR 图形的远程渲染消除了对本地个人计算机进行图形渲染的需求，并增强了独立 VR 耳机的弱图形处理单元容量。但是，为了防止远程渲染增加的网络延迟破坏用户体验，渲染一个大于 HMD 视野的本地可导航视口是必要的。所需视口的大小取决于延迟：更长的延迟需要渲染更大的视口并流式传输更多内容。在本文中，我们旨在利用多访问边缘计算来辅助后端云在这种远程渲染交互式 VR 中。鉴于延迟与流式传输内容的数量和质量之间的依赖关系，我们的目标是共同优化平均视频质量和交付延迟之间的权衡。将问题表述为混合整数非线性规划，我们利用客户端视场帧大小和整体延迟之间的插值将问题转换为整数非线性规划模型，然后设计有效的在线算法来解决它。我们的模拟结果加上真实世界的用户数据表明，在视频质量和延迟之间实现理想的平衡，我们的算法特别实现了平均约 22% 和 12% 的视频传输延迟和 8% 的改进视频质量分别与到达顺序、基于阈值和随机位置的策略进行比较。