  With increasing advancements in technologies for capturing 360{\deg} videos,
advances in streaming such videos have become a popular research topic.
However, streaming 360{\deg} videos require high bandwidth, thus escalating the
need for developing optimized streaming algorithms. Researchers have proposed
various methods to tackle the problem, considering the network bandwidth or
attempt to predict future viewports in advance. However, most of the existing
works either (1) do not consider video contents to predict user viewport, or
(2) do not adapt to user preferences dynamically, or (3) require a lot of
training data for new videos, thus making them potentially unfit for video
streaming purposes. We develop PARIMA, a fast and efficient online viewport
prediction model that uses past viewports of users along with the trajectories
of prime objects as a representative of video content to predict future
viewports. We claim that the head movement of a user majorly depends upon the
trajectories of the prime objects in the video. We employ a pyramid-based
bitrate allocation scheme and perform a comprehensive evaluation of the
performance of PARIMA. In our evaluation, we show that PARIMA outperforms
state-of-the-art approaches, improving the Quality of Experience by over 30\%
while maintaining a short response time.
