Improving streaming quality and bitrate efficiency with dynamic resolution selection

Abstract

Dynamic Resolution Selection is a technology that has been deployed by Netflix with its per-scene encoding mechanism applied to VOD assets. The technology is based on a posteriori analysis of all the encoded resolutions to determine the best resolution for a given scene, in terms of quality and bandwidth used, based on VMAF analysis. It cannot be applied to live content, as it would require too much processing power and can't be used in real time. The method proposed in this paper is based on a machine learning (ML) mechanism that learns how to pick the best resolution to be encoded in a supervised learning environment. At run time, using the already existing pre-processing stage, the live encoder can decide on the best resolution to encode, without adding any processing complexity or delay. This results in higher quality of experience (QoE) or lower bitrate, as well as lower CPU footprint vs. a classical fixed ladder approach. This paper will present the results obtained for live HD or 4K content delivery across different networks, including classical TS (DVB), native IP (ATSC 3.0) and ABR (DASH/HLS). In addition, the paper will report on the interoperability results of tested devices.