Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to introduce minimal transmission and computation overhead for mobile terminals. We also propose a model-agnostic meta-learning (MAML) based saliency prediction network trainer, which provides a few-sample fast training solution to obtain the prediction model by utilizing the information from the past models. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into a typical 360-degree video live streaming system by formulating and solving the bitrate adaptation problem. Extensive experiment results show that our prediction approach can work in real-time for live video streaming and can achieve higher accuracies compared to other existing prediction methods on mobile end, which, together with our bitrate adaptation algorithm, significantly improves the streaming QoE from various aspects. We observe the accuracy of MFVP is 8.1$\%$ to 28.7$\%$ higher than other algorithms and achieves 3.73$\%$ to 14.96$\%$ higher average quality level and 49.6$\%$ to 74.97$\%$ less quality level change than other algorithms.

翻译：视口预测是自适应360度视频流传输中的关键任务，因为码率控制算法通常需要了解用户观看帧的视角区域。目前，从精度较低的统计工具到高度校准的深度神经网络，多种视口预测方法已被研究和采用。传统上，在计算能力有限的移动设备上实现复杂的深度学习方法较为困难。本文提出了一种先进的基于学习的视口预测方法，并精心设计以最小化移动终端的传输和计算开销。同时，我们提出了一种基于模型无关元学习的显著性预测网络训练器，通过利用历史模型信息提供少量样本的快速训练方案来获取预测模型。我们进一步讨论了如何将该移动友好型视口预测方法集成到典型360度视频直播系统中，通过建模和求解码率自适应问题。大量实验结果表明，我们的预测方法能够实时应用于直播视频流，相比现有其他移动端预测方法具有更高精度，结合所提出的码率自适应算法，从多个维度显著提升了流媒体传输体验质量。观察表明，MFVP的精度比其他算法高8.1%至28.7%，平均质量水平提升3.73%至14.96%，质量水平变化幅度降低49.6%至74.97%。