Anti-Freeze: High-Quality Adaptive Live Streaming with Real-time Transcoder

Abstract

Video streaming constitutes more than 80% of Internet traffic [1], and the demand continues to rise as interactive video applications like virtual conferencing/collaboration as well as augmented/mixed reality emerge. Such interactive video applications pose a challenge for real-time video transcoding and streaming. Transcoding is a computationally intense process and if not performed efficiently it can result in unwanted delays, which further limit the Quality-of-Service (QoS) delivered by the streaming protocol. In this thesis, we aim to address the real-time challenge and propose Antifreeze, a complete end-to-end solution for real-time transcoding and streaming. We propose a machine-learning solution to estimate the transcoding time and resource requirement, which is used to direct our real-time transcoder to transcode video segments at the rate matching their frame rate. We complete the Antifreeze design with a novel quality adaptation algorithm that not only considers visual quality and bandwidth dynamics, but also transcoding time and necessary computing resource allocation. Our results show that Antifreeze significantly reduces the playback stalls and substantially improves the visual quality in interactive video streaming sessions under various bandwidth profiles.