  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.
