Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning

Abstract

HTTP Adaptive Streaming (HAS) is the de-facto solution for video delivery over the Internet. In HAS, each video is encoded at multiple quality levels and resolutions (i.e., representations) to enable adaptation of the streaming session to viewing and network conditions of the client. This requirement brings encoding challenges along with it, e.g., a video source should be encoded efficiently at multiple bitrates and resolutions. Fast multi-rate encoding approaches aim to address this challenge of encoding multiple representations from a single video by re-using information from already encoded representations. In this paper, a convolutional neural network is used to speed up both multi-rate and multi-resolution encoding for HAS. For multi-rate encoding, the lowest bitrate representation is chosen as the reference. For multi-resolution encoding, the highest bitrate from the lowest resolution representation is chosen as the reference. Pixel values from the target resolution and encoding information from the reference representation are used to predict Coding Tree Unit (CTU) split decisions in High-Efficiency Video Coding (HEVC) for dependent representations. Experimental results show that the proposed method for multi-rate encoding can reduce the overall encoding time by 15.08% and parallel encoding time by 41.26%, with a 0.89% bitrate increase compared to the HEVC reference software. Simultaneously, the proposed method for multi-resolution encoding can reduce the encoding time by 46.27% for the overall encoding and 27.71% for the parallel encoding on average with a 2.05% bitrate increase.

Keywords

• HTTP adaptive streaming
• HEVC
• multirate encoding
• machine learning