Cloud virtual reality (VR) gaming traffic characteristics such as frame size, inter-arrival time, and latency need to be carefully studied as a first step toward scalable VR cloud service provisioning. To this end, in this paper we analyze the behavior of VR gaming traffic and Quality of Service (QoS) when VR rendering is conducted remotely in the cloud. We first build a VR testbed utilizing a cloud server, a commercial VR headset, and an off-the-shelf WiFi router. Using this testbed, we collect and process cloud VR gaming traffic data from different games under a number of network conditions and fixed and adaptive video encoding schemes. To analyze the application-level characteristics such as video frame size, frame inter-arrival time, frame loss and frame latency, we develop an interval threshold based identification method for video frames. Based on the frame identification results, we present two statistical models that capture the behaviour of the VR gaming video traffic. The models can be used by researchers and practitioners to generate VR traffic models for simulations and experiments - and are paramount in designing advanced radio resource management (RRM) and network optimization for cloud VR gaming services. To the best of the authors' knowledge, this is the first measurement study and analysis conducted using a commercial cloud VR gaming platform, and under both fixed and adaptive bitrate streaming. We make our VR traffic data-sets publicly available for further research by the community.
翻译:云层虚拟现实( VR) 游戏交通特征, 如框架大小、 抵达间时间和延迟时间, 需要仔细研究, 作为向可缩放 VR 云层服务提供可缩放 VR 云层服务提供的第一步。 为此, 本文分析 VR 游戏流量和服务质量( QS) 在云层中远程进行 VR 传输时, 我们分析 VR 游戏流量和服务质量( Qos) 的行为。 我们首先利用云层服务器、 商业 VR 头目和 现成 WiFi 路由器等, 建立一个 VR 测试台。 使用这个测试台, 我们收集并处理不同游戏的云层 VR 监视数据数据数据数据数据数据数据。 研究人员和从业者可以使用这些模型来生成 VR 云层通信流量数据模型, 进行模拟和适应性网络的最佳商业数据测试 。 VRM 数据库的高级数据采集者 正在设计我们VR 数据库的高级数据库 。