This document discusses machine learning based enhancements for video coding and HTTP adaptive streaming. It introduces the research questions around efficiently providing multi-rate video representations over different resolutions for adaptive streaming, improving video codec performance with machine learning, improving video quality with machine learning, and using machine learning for perceptual quality assessment. It outlines the methodology, design process, and existing results from papers on fast multi-rate encoding using information from reference representations and machine learning models. Ongoing and future work is focused on super-resolution, perceptual quality assessment with machine learning, and improving in-loop filtering with machine learning.