Analysis of 360° Video Viewing Behaviours

In recent years there has been a significant interest in Virtual Reality applications, including 360° videos. 360° video streaming via the network typically consumes a lot of bandwidth due to the file size of these kind of videos being up to 6 times larger than traditional videos. Due to the nature of 360° videos, a user only utilizes about 20 % of the transmitted data; that is, the part of the video that is actually viewed by the user at any given moment. This makes it unnecessary to send an entire video file and a view-dependent streaming strategy could greatly improve the situation by only sending the parts of the video that is relevant to the user. Such a strategy could benefit from information about users' 360° viewing behaviour in order to compensate for quick viewing motions which could lead to the user viewing parts of the video that have not been transmitted. A view-dependent streaming approach could therefore allow for a reduction in bandwidth while maintaining a low rate of error if based on information about 360° viewing behaviours. In this thesis we study users' viewing motions when watching 360° videos in order to provide information that can be used to optimize future view-dependent streaming protocols. More specifically, we develop an application that plays a sequence of 360° videos on an Oculus Rift Head Mounted Display and records the orientation and rotation velocity of the headset during playback. The application is used during an extensive user study in order to collect more than 21 hours of viewing data which is then analysed to expose viewing patterns, useful for optimizing 360° streaming protocols. The results from the analysis shows that video content highly affects the viewers of a video during playback. When a video has a clear point of focus, it is quite possible to predict where user's will look at certain points in time. Moreover, for short time intervals it is highly possible to predict how a user's viewpoint is likely to change as the head rotations will be small with a very high accuracy. For longer time intervals a user's viewpoint is likely to rotate to almost anywhere in the 360° sphere making it difficult to make fine-grained predictions. Additionally, how a user's viewpoint is likely to change can be more easily predicted if one also considers the current rotation velocity, where the viewpoint is located in the sphere and where in the duration of a video the playback currently is.