QoE驱动的VR视频自适应采集与传输

在虚拟现实（VR）视频流媒体传输中，如何在带宽受限的条件下提高用户的质量体验（QoE）是一项巨大的挑战。为了更好地提高资源利用率和用户的QoE，提出了一个面向多用户的QoE驱动上下行链路联合的VR视频流媒体自适应采集与传输系统。与传统的VR视频无线传输系统不同的是，所提系统考虑了上行传输部分。其中，视频服务器根据上行链路和下行链路的带宽信息、用户的实时视角信息，以速率自适应为基础进行码率选择和资源分配。定义了QoE驱动的码率选择和资源分配问题，以最大化整个系统所有用户的QoE值。提出了联合KKT条件和分支定界法的速率自适应选择算法。实验结果表明:所提系统可以有效提高所有用户的QoE值，与上行链路平均分配资源算法相比，系统QoE值提高了14.27%，同时与传统的VR视频速率自适应算法相比，系统QoE值提高了23.47%。 

QoE driven adaptation for VR video capturing and transmission

In virtual reality (VR) video streaming media transmission, how to further improve user's quality of experience (QoE) under bandwidth-constrained conditions is a huge challenge. In order to improve resource utilization rate and user QoE, a multi-user QoE-driven uplink and downlink joint VR video streaming media adaptive acquisition and transmission system is proposed, which is different from the traditional VR video wireless transmission system. The proposed system considers the uplink transmission part. The video server selects code rate and allocates resources based on the rate adaptation based on the bandwidth information of the uplink channel and the downlink channel and the real-time view information of the user. In addition, we define the problem of QoE-driven rate selection and resource allocation to maximize the QoE value of all users across the system. Finally, we propose an optimal adaptive rate selection algorithm combining the KKT condition and the branch and bound method. The experimental results show that the system can effectively improve the QoE value of the total system users, improve the system performance by 14.27% based on the average uplink allocation, and improve the performance of the VR video rate adaptive algorithm by 23.47%.