The Sixth IEEE International Conference on Multimedia Big Data (BigMM 2020)
Authors: Anatoliy Zabrovskiy (Alpen-Adria-Universitat Klagenfurt), Prateek Agrawal (Alpen-Adria-Universitat Klagenfurt, Lovely Professional University), Roland Matha (Alpen-Adria-Universitat Klagenfurt), Christian Timmerer (Alpen-Adria-Universitat Klagenfurt, Bitmovin) and Radu Prodan (Alpen-Adria-Universitat Klagenfurt).
Abstract: HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.
Keywords: Transcoding time prediction, adaptive streaming, video transcoding, neural networks, video encoding, video complexity class, MPEG-DASH