In this paper, we present a statistical characterization of tile decoding time of 360° videos encoded via HEVC that considers different tiling patterns and quality levels (i.e., bitrates). In particular, we present results for probability density function estimation of tile decoding time based on a series of experiments carried out over a set of 360° videos with different spatial and temporal characteristics. Additionally, we investigate the extent to which tile decoding time is correlated with tile bitrate (at chunk level), so that DASH-based video streaming can make possible use of such an information to infer tile decoding time. The results of this work may help in the design of queueing or control theory-based adaptive bitrate (ABR) algorithms for 360° video streaming.
Measuring Quality of Experience (QoE) and integrating these measurements into video streaming algorithms is a multi-faceted problem that fundamentally requires the design of comprehensive subjective QoE databases and metrics. To achieve this goal, we have recently designed the LIVE-NFLX-II database, a highly-realistic database which contains subjective QoE responses to various design dimensions, such as bitrate adaptation algorithms, network conditions and video content. Our database builds on recent advancements in content-adaptive encoding and incorporates actual network traces to capture realistic network variations on the client device. Using our database, we study the effects of multiple streaming dimensions on user experience and evaluate video quality and quality of experience models. We believe that the tools introduced here will help inspire further progress on the development of perceptuallyoptimized client adaptation and video streaming strategies.