  360-degree panoramic videos have gained considerable attention in recent
years due to the rapid development of head-mounted displays (HMDs) and
panoramic cameras. One major problem in streaming panoramic videos is that
panoramic videos are much larger in size compared to traditional ones.
Moreover, the user devices are often in a wireless environment, with limited
battery, computation power, and bandwidth. To reduce resource consumption,
researchers have proposed ways to predict the users' viewports so that only
part of the entire video needs to be transmitted from the server. However, the
robustness of such prediction approaches has been overlooked in the literature:
it is usually assumed that only a few models, pre-trained on past users'
experiences, are applied for prediction to all users. We observe that those
pre-trained models can perform poorly for some users because they might have
drastically different behaviors from the majority, and the pre-trained models
cannot capture the features in unseen videos. In this work, we propose a novel
meta learning based viewport prediction paradigm to alleviate the worst
prediction performance and ensure the robustness of viewport prediction. This
paradigm uses two machine learning models, where the first model predicts the
viewing direction, and the second model predicts the minimum video prefetch
size that can include the actual viewport. We first train two meta models so
that they are sensitive to new training data, and then quickly adapt them to
users while they are watching the videos. Evaluation results reveal that the
meta models can adapt quickly to each user, and can significantly increase the
prediction accuracy, especially for the worst-performing predictions.
