  Adaptive bitrate (ABR) streaming is the de facto solution for achieving
smooth viewing experiences under unstable network conditions. However, most of
the existing rate adaptation approaches for ABR are content-agnostic, without
considering the semantic information of the video content. Nevertheless,
semantic information largely determines the informativeness and interestingness
of the video content, and consequently affects the QoE for video streaming. One
common case is that the user may expect higher quality for the parts of video
content that are more interesting or informative so as to reduce video
distortion and information loss, given that the overall bitrate budgets are
limited. This creates two main challenges for such a problem: First, how to
determine which parts of the video content are more interesting? Second, how to
allocate bitrate budgets for different parts of the video content with
different significances? To address these challenges, we propose a
Content-of-Interest (CoI) based rate adaptation scheme for ABR. We first design
a deep learning approach for recognizing the interestingness of the video
content, and then design a Deep Q-Network (DQN) approach for rate adaptation by
incorporating video interestingness information. The experimental results show
that our method can recognize video interestingness precisely, and the bitrate
allocation for ABR can be aligned with the interestingness of video content
while not compromising the performances on objective QoE metrics.
