论文标题
QOE驱动点云视频流的联合通信和计算资源分配
Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming
论文作者
论文摘要
Point Cloud视频是全息图最受欢迎的表示，它是VR/AR/MR中先例自然内容的媒介，预计将是下一代视频。 Point Cloud Video系统为用户提供了具有六个自由度的沉浸式观看体验，并且在在线教育，娱乐等许多领域中都有广泛的应用。为了进一步增强这些应用程序，点云视频流是关键需求。固有的挑战在于大小，除了颜色信息以外，还需要记录三维坐标以及相关的高计算复杂性。为此，本文提出了针对QoE驱动的点云视频流的通信和计算资源分配方案。特别是，我们通过选择不同数量，传输形式和质量水平瓷砖以最大化体验质量来最大化系统资源利用率。进行了广泛的仿真，模拟结果表明，与现有方案相比的性能优越
Point cloud video is the most popular representation of hologram, which is the medium to precedent natural content in VR/AR/MR and is expected to be the next generation video. Point cloud video system provides users immersive viewing experience with six degrees of freedom and has wide applications in many fields such as online education, entertainment. To further enhance these applications, point cloud video streaming is in critical demand. The inherent challenges lie in the large size by the necessity of recording the three-dimensional coordinates besides color information, and the associated high computation complexity of encoding. To this end, this paper proposes a communication and computation resource allocation scheme for QoE-driven point cloud video streaming. In particular, we maximize system resource utilization by selecting different quantities, transmission forms and quality level tiles to maximize the quality of experience. Extensive simulations are conducted and the simulation results show the superior performance over the existing schemes
