Sparkle: User-Aware Viewport Prediction in 360-Degree Video Streaming
Sparkle: User-Aware Viewport Prediction in 360-Degree Video Streaming
复制标题
Sparkle：360 度视频流中的用户感知视口预测
DOI：
10.1109/tmm.2020.3033127
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发表时间：
2021
影响因子：
7.3
通讯作者：
Zhou Yipeng
中科院分区：
文献类型：
--
作者：
Chen Jinyu;Luo Xianzhuo;Hu Miao;Wu Di;Zhou Yipeng
在 360 度视频流中，用户通常在视场 (FoV) 内观看视频场景。这种观察提供了通过预测然后预取 FoV 内的视频块来节省带宽消耗的机会。然而，现有的视场预测方法很少考虑用户行为的多样性以及不同视频类型的影响。因此，以前的一刀切模型无法对不同行为模式的用户做出准确的预测。在本文中，我们提出了一种名为 Sparkle 的用户感知视口预测算法，这是一种实用的 FoV 预测白盒方法。我们提出的算法不是训练单个学习模型来预测所有用户的行为，而是针对每个用户进行定制。特别是，与其他学习模型不同，我们的预测模型是完全可解释的，并且所有参数都有其物理含义。我们首先进行测量研究来分析真实的用户行为，并观察到视图方向和用户姿势存在剧烈波动对用户的视口移动有显着影响。此外，不同视频类型的跨用户相似性是不同的。受这些见解的启发，我们通过模仿用户在图块地图上的视口移动，进一步设计了一种用户感知的视口预测算法，并根据用户在过去时间窗口中的轨迹和其他类似用户的行为来确定用户将如何改变视口角度。对真实数据集的广泛评估表明，我们提出的算法明显优于最先进的基准方法（例如基于 LSTM 的方法）超过 $\text{5}\%$，并且在各种类型的 360 度视频上的预测精度比以前的方法更加稳定。
In 360-degree video streaming, users commonly watch a video scene within a Field of View (FoV). Such observation provides an opportunity to save bandwidth consumption by predicting and then prefetching video tiles within the FoV. However, existing FoV prediction methods seldom consider the diversity among user behaviors and the impact of different video genres. Thus, previous one-size-fits-all models cannot make accurate prediction for users with different behavior patterns. In this paper, we propose a user-aware viewport prediction algorithm called Sparkle, which is a practical whitebox approach for FoV prediction. Instead of training a single learning model to predict the behaviors for all users, our proposed algorithm is tailored to fit each individual user. In particular, unlike other learning models, our prediction model is completely explainable and all the parameters have their physical meanings. We first conduct a measurement study to analyze real user behaviors and observe that there exists sharp fluctuation of view orientation and user posture has significant impact on the viewport movement of users. Moreover, cross-user similarity is diverse across different video genres. Inspired by these insights, we further design a user-aware viewport prediction algorithm by mimicking a user's viewport movement on the tile map, and determine how a user will change the viewport angle based on his (or her) trajectory and other similar users’ behaviors in the past time window. Extensive evaluations with real datasets demonstrate that, our proposed algorithm significantly outperforms the state-of-the-art benchmark methods (e.g., LSTM-based methods) by over $\text{5}\%$, and the prediction accuracy is much more stable on various types of 360-degree videos than previous methods.