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Short videos have recently emerged as a popular form of short- duration User Generated Content (UGC) within modern social me- dia. Short video content is generally less than a minute long and predominantly produced in vertical orientation on smartphones. While still fundamentally being streaming, short video delivery is distinctly characterized by the deployment of a mechanism that pre-loads ahead of user request. Background pre-loading aims to eliminate start-up time, which is now prioritized higher in Quality of Experience (QoE) objectives, given that the application design facilitates instant ‘swiping’ to the next video in a recommended sequence. In this work, we provide a comprehensive comparison of four popular short video services. In particular, we explore content characteristics and evaluate the video quality across resolutions for each service. We next characterize the pre-loading policy adopted by each service. Last, we conduct an experimental study to investi- gate data consumption and evaluate achieved QoE under different network scenarios and application configurations. 

WiFi has emerged as a pivotal technology for delivering Quality of Experience (QoE) to mobile devices. Unfortunately, exploding numbers of competing devices, potential encroachment by cellular technology, and dramatic increases in content richness deliver a more variable QoE than desired. Moreover, such variance tends to occur both across time and space making it a difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to suffer from accuracy issues. In our paper, we propose a novel passive client-side approach that provides an efficient and accurate characterization by taking advantage of the properties of Frame Aggregation (FA) and Block Acknowledgements (BA). We show in the paper that one can accurately derive important metrics such as airtime and throughput with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate. 

Understanding end-user video Quality of Experience (QoE) is important for Internet Service Providers (ISPs). Existing work presents mechanisms that use network measurement data to estimate video QoE. Most of these mechanisms assume access to packet-level traces, the most-detailed data available from the network. However, collecting packet-level traces can be challenging at a network-wide scale. Therefore, we ask: "Is it feasible to estimate video QoE with lightweight, readily-available, but coarse-grained network data?" We specifically consider data in the form of Transport Layer Security (TLS) transactions that can be collected using a standard proxy and present a machine learning-based methodology to estimate QoE. Our evaluation with three popular streaming services shows that the estimation accuracy using TLS transactions is high (up to 72%) with up to 85% recall in detecting low QoE (low video quality or high re-buffering) instances. Compared to packet traces, the estimation accuracy (recall) is 7% (9%) lower but has up to 60 times lower computation overhead.