A QoE Aware SVC Based Client-side Video Adaptation Algorithm for Cellular Networks

Abstract

The ever-increasing demand for video streaming is set to pose considerable challenges on the state-of-the-art multimedia transmission technologies like Dynamic Adaptive Streaming over HTTP (DASH). In this work, we propose an efficient client-side adaptation algorithm for the DASH framework that attempts to maximize the QoE of an end user by simultaneously balancing three important QoE verticals: (i) providing stutter-free video viewing experience, (ii) minimizing flickers in video outputs by controlling and smoothing the rates of encoding quality switches over time and (iii) maximizing aggregate video quality over an entire video session. In order to maintain uninterrupted high quality video viewing experience, the adaptation strategy dynamically selects the enhancement levels for video segments based on instantaneous network conditions and playout buffer status. We have evaluated the proposed adaptation strategy through theoretical analysis and simulation based experiments. Simulation results reveal that the proposed scheme is able to restrict re-buffering events significantly while simultaneously achieving high video quality and stability in the video output.