  Virtual reality has been gaining popularity in recent years caused by the
proliferation of affordable consumer-grade devices such as Oculus Rift, HTC
Vive, and Samsung VR. Amongst the various VR applications, 360{\deg} video
streaming is currently one of the most popular ones. It allows user to change
their field-of-view (FoV) based on head movement, which enables them to freely
select an area anywhere from the sphere the video is (virtually) projected to.
While 360{\deg} video streaming offers new exciting ways of consuming content
for viewers, it poses a series of challenges to the systems that are
responsible for the distribution of such content from the origin to the viewer.
One challenge is the significantly increased bandwidth requirement for
streaming such content in real time. Recent research has shown that only
streaming the content that is in the user's FoV in high quality can lead to
strong bandwidth savings. This can be achieved by analyzing the viewers head
orientation and movement based on sensor information. Alternatively, historic
information from users that watched the content in the past can be taken into
account to prefetch 360{\deg} video data in high quality assuming the viewer
will direct the FoV to these areas. In this paper, we present a 360{\deg} video
streaming system that transitions between sensor- and content-based predictive
mechanisms. We evaluate the effects of this transition-based approach on the
Quality of Experience (QoE) of such a VR streaming system and show that the
perceived quality can be increased between 50\% and 80\% compared to systems
that only apply either one of the two approaches.
