TVG-Streaming: Learning User Behaviors for QoE-Optimized 360-Degree Video Streaming
TVG-Streaming: Learning User Behaviors for QoE-Optimized 360-Degree Video Streaming
复制标题
TVG-Streaming：学习用户行为以实现 QoE 优化的 360 度视频流
DOI：
10.1109/tcsvt.2020.3046242
复制
发表时间：
2021-10
影响因子：
8.4
通讯作者：
Dai Hong-Ning
中科院分区：
文献类型：
--
作者：
Hu Miao;Chen Jiawen;Wu Di;Zhou Yipeng;Wang Yi;Dai Hong-Ning
360-度视频流显示了巨大的潜力，革命性的流媒体市场，通过提供更好的沉浸式体验比标准的视频流。然而，由于多屏幕视频传输对网络带宽的需求激增，阻碍了其广泛采用。为了降低带宽成本，一种有前途的方法是预测用户的视场（FoV），然后预取用户将提前几秒观看的视频区块。挑战在于，用户行为不能用非常有限的信息正确地捕获，特别是在每个图块上花费的观看时间和FoV切换行为难以预测。在本文中，我们提出了一种新的360度视频流算法称为TVG-Streaming，通过学习用户的观看行为来优化用户体验。与以前的方法不同，我们的想法是利用由真实的用户行为生成的瓦片视图图（TVG），并准确地估计每个瓦片福尔斯落在FoV中的概率。通过瓦片观看概率，我们可以确定每个瓦片的比特率，以在有限的带宽预算下进行交付和缓冲，从而最大化用户的体验质量（QoE）。为了评估，我们进行了广泛的实验，使用真实的痕迹和结果表明，我们提出的TVG-Streaming算法显着优于其他算法至少20%的改善用户的QoE。
360-degree video streaming shows great potential to revolutionize the streaming market, by providing much better immersive experience than standard video streams. However, its wide adoption is hindered by the surging demand of network bandwidth due to multi-screen video transmission. To reduce the bandwidth cost, one promising approach is to predict a user’s field of view (FoV), and then prefetch video tiles that a user will view a few seconds ahead. The challenge lies in that user behaviors cannot be properly captured with very limited information, especially the viewing time spent on each tile and the FoV switching behavior are hard to predict. In this paper, we propose a novel 360-degree video streaming algorithm called TVG-Streaming to optimize user experiences by learning user view behaviors. Different from previous approaches, our idea is to exploit tile-view graphs (TVGs) generated by real user behaviors and accurately estimate the probability that each tile falls in the FoV. With the tile view probability, we can determine the bitrate of each tile for delivery and buffering with limited bandwidth budget so as to maximize users’ quality of experience (QoE). For evaluation, we conduct extensive experiments using real traces and the results show that our proposed TVG-Streaming algorithm significantly outperforms other algorithms by at least 20% improvement in terms of users’ QoE.