FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning
Ekrem Çetinkaya, Hadi Amirpour, Christian Timmerer, and Mohammad Ghanbari
Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt
Abstract: HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. The requirement to encode the same content at different quality levels (i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.
Keywords: Video Coding, Convolutional Neural Networks, HEVC, HTTP Adaptive Streaming (HAS)
Acknowledgments: The financial support of the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Christian Doppler Research Association, is gratefully acknowledged. Christian Doppler Laboratory ATHENA: https://athena.itec.aau.at/.