KEYWORDS: Video, Computer programming, Video coding, Optimization (mathematics), Statistical modeling, Video compression, Televisions, Performance modeling, Systems modeling, Video processing
We propose a framework for popularity-driven rate allocation in H.264/MVC-based multi-view video communications
when the overall rate and the rate necessary for decoding each view are constrained in the delivery
architecture. We formulate a rate allocation optimization problem that takes into account the popularity of
each view among the client population and the rate-distortion characteristics of the multi-view sequence so that
the performance of the system is maximized in terms of popularity-weighted average quality. We consider the
cases where the global bit budget or the decoding rate of each view is constrained. We devise a simple ratevideo-
quality model that accounts for the characteristics of interview prediction schemes typical of multi-view
video. The video quality model is used for solving the rate allocation problem with the help of an interior
point optimization method. We then show through experiments that the proposed rate allocation scheme clearly
outperforms baseline solutions in terms of popularity-weighted video quality. In particular, we demonstrate that
the joint knowledge of the rate-distortion characteristics of the video content, its coding dependencies, and the
popularity factor of each view is key in achieving good coding performance in multi-view video systems.
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