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Adaptive video streaming relies on the construction of efficient bitrateladders to deliver the best possible visual quality to viewers under bandwidthconstraints. The traditional method of content dependent bitrate ladderselection requires a video shot to be pre-encoded with multiple encodingparameters to find the optimal operating points given by the convex hull of theresulting rate-quality curves. However, this pre-encoding step is equivalent toan exhaustive search process over the space of possible encoding parameters,which causes significant overhead in terms of both computation and timeexpenditure. To reduce this overhead, we propose a deep learning based methodof content aware convex hull prediction. We employ a recurrent convolutionalnetwork (RCN) to implicitly analyze the spatiotemporal complexity of videoshots in order to predict their convex hulls. A two-step transfer learningscheme is adopted to train our proposed RCN-Hull model, which ensuressufficient content diversity to analyze scene complexity, while also making itpossible to capture the scene statistics of pristine source videos. Ourexperimental results reveal that our proposed model yields betterapproximations of the optimal convex hulls, and offers competitive time savingsas compared to existing approaches. On average, the pre-encoding time wasreduced by 53.8% by our method, while the average Bjontegaard delta bitrate(BD-rate) of the predicted convex hulls against ground truth was 0.26%, and themean absolute deviation of the BD-rate distribution was 0.57%.

Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning