Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation Framework

Abstract

We present an open-source cloud-based adaptive video streaming evaluation framework; a testbed that can be instantiated in a cloud infrastructure, examine multiple media players with different network profiles, and conclude the evaluation with statistics and insights into the result [3]. It has been used in multiple projects to evaluate the performance of media players' Adaptive Bitrate (ABR) algorithms and to conduct subjective evaluations to compare with known QoE models like ITU-T P.1203. We used this framework to address unanswered questions such as (i) the minimum noticeable duration for stall events in HAS; (ii) the correlation between the media quality and the impact of stall events on QoE; (iii) the end-user preference regarding multiple shorter stall events versus a single longer stall event; and (iv) the end-user preference of media quality switches over stall events [2].