A User-experience Driven SSIM-Aware Adaptation Approach for DASH Video Streaming
This work provides an incremental improvement in video streaming quality of experience for users of DASH services by reducing rebuffering and instability.
This paper proposes a new adaptation algorithm for DASH video streaming, called SSIM Based Adaptation (SBA), which uses the Structural Similarity Index Measurement (SSIM) as a perceptual quality indicator. The SBA algorithm minimizes rebuffering and video quality instability while maximizing overall video quality, outperforming representative algorithms like BBA, FESTIVE, and OSMF in trace-driven emulations.
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.