Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming
arxiv(2024)
摘要
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.
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