MPEG-DASH users quality of experience enhancement for MOOC videos

Abstract

The Dynamic Adaptive Streaming over HTTP (MPEG-DASH) ensures online videos display of good quality and without interruption. It provides an adequate streaming for each display device and network transmission. This can be very useful for the specific field of Massive Open Online Courses (MOOCs) where learners profit from an exceptional visual experience that improves their commitment level and eases the course assimilation. These MPEG-DASH assets can become more and more advantageous if a good choice of its parameters is made. Being a recent branch, the MPEG-DASH adaptive diffusion presents a research field where the efforts are still limited, even more for MOOC videos. Most of the work published in this sense focus on the Quality of Service (QoS) and the technical specifications of the network transmission. In this paper, we aim to consider the quality of the streamed content that directly impacts the learners quality of Experience (QoE). For this, we develop a content-aware dataset that includes several dashified MOOC videos. These latter are then exploited to study the most appropriate bitrates and segment durations for each type of MOOC videos.