论文标题
连续增强的360度视频自适应流与交叉用户细心网络
Sequential Reinforced 360-Degree Video Adaptive Streaming with Cross-user Attentive Network
论文作者
论文摘要
在基于瓷砖的360度视频流中，预测用户的未来观点和开发自适应比特量（ABR）算法对于优化用户的体验质量（QOE）至关重要。传统的基于单用户的观点预测方法无法在长期预测中实现良好的性能，并且由于指数动作领域的指数动作空间，无法直接应用于传统视频流中采用的基于传统视频流中的ABR方案（RL）的ABR方案。因此，我们提出了一个使用交叉用户专注网络的连续增强360度视频流方案。首先，考虑到不同的用户可能对同一视频具有相似的观看偏好，我们提出了一个跨用户专注网络（CUAN），从而通过选择性地利用跨用户信息来提高长期观点预测的性能。其次，我们提出了一种基于顺序的RL（360SRL）ABR方法，通过引入顺序决策结构将每个决策步骤的动作空间大小从指数转换为线性。我们使用痕量驱动的实验评估了提出的铜和360SRL，实验结果表明，Cuan和360SRL的表现优于现有的观点预测和ABR接近，并具有明显的余量。
In the tile-based 360-degree video streaming, predicting user's future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user's quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin.
