The 360-degree video streaming has higher bandwidth requirements compared with traditional video to achieve the same user-perceived playback quality. Since users only view part of the entire videos, viewport-adaptive streaming is an effective approach to guarantee video quality. However, the performance of viewport-adaptive schemes is highly dependent on the bandwidth estimation and viewport prediction. To overcome these issues, we propose a novel reinforcement learning (RL) based viewport-adaptive streaming framework called RLVA, which optimizes the 360-degree video streaming in viewport prediction, prefetch scheduling and rate adaptation. Firstly, RLVA adopts ${t}$ location-scale distribution rather than Gaussian distribution to describe the viewport prediction error characteristic more accurately and achieve the tile viewing probability based on the distribution. Besides, a tile prefetch scheduling algorithm is proposed to update the tiles according to the latest prediction results, which further reduces the adverse effect of prediction error. Furthermore, the tile viewing probabilities are treated as input status of RL algorithm. In this way, RL can adjust its policy to adapt to both of the network conditions and viewport prediction error. Through extensive evaluations, the simulation results show that the proposed RLVA outperforms other viewport-adaptive methods by about 4.8%-66.8% improvement of Quality of Experience (QoE) and effectively reduces the impact of viewport prediction errors.

中文翻译：

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基于强化学习的 360 度视频流速率自适应

与传统视频相比，360 度视频流具有更高的带宽要求，以实现相同的用户感知播放质量。由于用户只能观看整个视频的一部分，因此视口自适应流是保证视频质量的有效方法。然而，视口自适应方案的性能高度依赖于带宽估计和视口预测。为了克服这些问题，我们提出了一种称为 RLVA 的新型基于强化学习 (RL) 的视口自适应流媒体框架，它在视口预测、预取调度和速率自适应方面优化了 360 度视频流。首先，RLVA采用 ${t}$ 位置尺度分布而不是高斯分布来更准确地描述视口预测误差特征，并根据分布实现瓦片查看概率。此外，提出了一种瓦片预取调度算法，根据最新的预测结果更新瓦片，进一步降低了预测误差的不利影响。此外，瓦片查看概率被视为 RL 算法的输入状态。通过这种方式，RL 可以调整其策略以适应网络条件和视口预测误差。通过广泛的评估，仿真结果表明，所提出的 RLVA 优于其他视口自适应方法，提高了约 4.8%-66.8% 的体验质量 (QoE)，并有效降低了视口预测错误的影响。