A User-experience Driven SSIM-Aware Adaptation Approach for DASH Video Streaming

TL;DR

This paper introduces a new DASH adaptation algorithm called SSIM Based Adaptation (SBA) that uses SSIM as a key quality indicator, aiming to improve user experience by reducing rebuffering and quality fluctuations.

Contribution

The paper presents a novel SSIM-aware adaptation algorithm for DASH that jointly considers perceptual quality and network resources to enhance streaming quality.

Findings

SBA reduces rebuffering and quality instability compared to existing algorithms.

SBA maintains higher video quality levels during streaming.

Experimental results show SBA outperforms BBA, FESTIVE, and OSMF in QoE metrics.

Abstract

Dynamic Adaptive Streaming over HTTP (DASH) is a video streaming technique largely used. One key point is the adaptation mechanism which resides at the client's side. This mechanism impacts greatly on the overall Quality of Experience (QoE) of the video streaming. In this paper, we propose a new adaptation algorithm for DASH, namely SSIM Based Adaptation (SBA). This mechanism is user-experience driven: it uses the Structural Similarity Index Measurement (SSIM) as main video perceptual quality indicator; moreover, the adaptation is based on a joint consideration of SSIM indicator and the physical resources (buffer occupancy, bandwidth) in order to minimize the buffer starvation (rebuffering) and video quality instability, as well as to maximize the overall video quality (through SSIM). To evaluate the performance of our proposal, we carried out trace-driven emulation with real traffic traces (captured in real mobile network). Comparisons with some representative algorithms (BBA, FESTIVE, OSMF) through major QoE metrics show that our adaptation algorithm SBA achieves an efficient adaptation minimizing both the rebuffering and instability, whereas the displayed video is maintained at a high level of bitrate.