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Field of view (FoV) prediction is critical in 360-degree video multicast,which is a key component of the emerging Virtual Reality (VR) and AugmentedReality (AR) applications. Most of the current prediction methods combiningsaliency detection and FoV information neither take into account that thedistortion of projected 360-degree videos can invalidate the weight sharing oftraditional convolutional networks, nor do they adequately consider thedifficulty of obtaining complete multi-user FoV information, which degrades theprediction performance. This paper proposes a spherical convolution-empoweredFoV prediction method, which is a multi-source prediction framework combiningsalient features extracted from 360-degree video with limited FoV feedbackinformation. A spherical convolution neural network (CNN) is used instead of atraditional two-dimensional CNN to eliminate the problem of weight sharingfailure caused by video projection distortion. Specifically, salientspatial-temporal features are extracted through a spherical convolution-basedsaliency detection model, after which the limited feedback FoV information isrepresented as a time-series model based on a spherical convolution-empoweredgated recurrent unit network. Finally, the extracted salient video features arecombined to predict future user FoVs. The experimental results show that theperformance of the proposed method is better than other prediction methods.

Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback