Video streaming applications (VSAs) are increasingly being deployed on large-scale edge platforms, which have the potential to significantly improve the quality of service (QoS) and end-user experience (QoE), ultimately maximizing business outcomes. However, there is currently very little understanding of how QoS, QoE, and the impact of QoS on QoE for VSAs on edge platforms in the wild and at scale. To close the knowledge gap, we collect SNESet, an active measurement dataset comprising QoS and QoE telemetry metrics of 8 VSAs over four months, covering end-users from 798 edge sites,30 cities, and 3 ISPs in one country.We characterize and compare the QoS and QoE metrics in SNESet with existing publicly available datasets, highlighting that SNESet includes a significantly greater number of metrics (horizontal diversity and vertical hierarchy) and provides more comprehensive coverage of specific metrics.Moreover, we qualitatively and quantitatively analyze the impact of QoS on QoE in both domain-general and domain-specific scenarios. Our findings can inform the system design decisions that different entities in the video ecosystem (content providers, video player designers, third-party optimizers, edge vendors) make to maximize end-users experience and ultimately maximize the business outcomes. We hope SNESet can attract more research efforts in the data management community, computer network community, and beyond.

In P2P video-on-demand (VoD) streaming system, aggregate download bandwidth of users fluctuates significantly because of peer departure, bandwidth heterogeneity and network delay. The playback rate of videos also fluctuates in order to attain constant image quality. These two kinds of dynamics cause playback interruptions. Prefetching some video content before playback is an effective method to offset the adverse effect of dynamics. In this paper, we choose the number of interruptions in a viewing process as the metric of user perceived QoS (UPQoS). Then we propose a stochastic model to relate the UPQoS with some important system parameters, such as playback rate, downloading speed and prefetched content volume (PCV). It is found that reducing the system dynamics or increasing the PCV or aggregate download bandwidth can improve UPQoS. Besides, the interruption occurs at the beginning of a viewing process with great probability; a few users suffer many interruptions in a viewing process while most of users experience no interruption. Finally, we do a large scale network measurement towards PPLive VoD streaming system and the measurement result is consistent with the conclusion got from this model.