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360-degree panoramic videos have gained considerable attention in recentyears due to the rapid development of head-mounted displays (HMDs) andpanoramic cameras. One major problem in streaming panoramic videos is thatpanoramic videos are much larger in size compared to traditional ones.Moreover, the user devices are often in a wireless environment, with limitedbattery, computation power, and bandwidth. To reduce resource consumption,researchers have proposed ways to predict the users' viewports so that onlypart of the entire video needs to be transmitted from the server. However, therobustness 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 thosepre-trained models can perform poorly for some users because they might havedrastically different behaviors from the majority, and the pre-trained modelscannot capture the features in unseen videos. In this work, we propose a novelmeta learning based viewport prediction paradigm to alleviate the worstprediction performance and ensure the robustness of viewport prediction. Thisparadigm uses two machine learning models, where the first model predicts theviewing direction, and the second model predicts the minimum video prefetchsize that can include the actual viewport. We first train two meta models sothat they are sensitive to new training data, and then quickly adapt them tousers while they are watching the videos. Evaluation results reveal that themeta models can adapt quickly to each user, and can significantly increase theprediction accuracy, especially for the worst-performing predictions.

Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality