Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming
Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming
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DOI：
10.1109/icc40277.2020.9148922
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Jie Li;Cong Zhang;Zhi Liu;Wei Sun;Qiyue Li
中科院分区：
文献类型：
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作者：
Jie Li;Cong Zhang;Zhi Liu;Wei Sun;Qiyue Li
点云视频是最流行的全息图表现形式，是VR/AR/MR中先行自然内容的媒介，有望成为下一代视频。点云视频系统为用户提供六自由度（6DoF）的沉浸式观看体验，在在线教育、娱乐等诸多领域有着广泛的应用。为了进一步增强这些应用，对点云视频流的需求至关重要。其固有的挑战在于，除了颜色信息外，还需要记录三维坐标，因此尺寸较大，并且编码/解码的计算复杂度较高。为此，本文提出了一种基于qos驱动的点云视频流通信和计算资源分配方案。特别是，为了通过对每个分割的点云视频块选择合适的质量级别（也考虑不同质量级别的未压缩块）来最大化定义的QoE，我们将其转化为有限通信和计算资源约束下的优化问题，并提出了一种解决方案。进行了大量的仿真，仿真结果表明该方案优于现有方案。
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 (6DoF) and has wide applications in many fields such as online education and 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/decoding. To this end, this paper proposes a communication and computational resource allocation scheme for QoE-driven point cloud video streaming. In particular, with the goal to maximize the defined QoE by selecting proper quality levels (uncompressed tiles at different quality levels are also considered) for each partitioned point cloud video tile, we formulate this into an optimization problem under the limited communication and computational resources constraints and propose a scheme to solve it. Extensive simulations are conducted and the simulation results show the superior performance of the proposed scheme over the existing schemes.