The availability of high definition video content on the web has brought about a significant change in the characteristics of Internet video, but not many studies on characterizing video have been done after this change. Video characteristics such as video length, format, target bit rate, and resolution provide valuable input to design Adaptive Bit Rate (ABR) algorithms, sizing playout buffers in Dynamic Adaptive HTTP streaming (DASH) players, model the variability in video frame sizes, etc. This paper presents datasets collected in 2013 and 2014 that contains over 130,000 videos from YouTube's most viewed (or most popular) video charts in 58 countries. We describe the basic characteristics of the videos on YouTube for each category, format, video length, file size, and data rate variation, observing that video length and file size fit a log normal distribution. We show that three minutes of a video suffice to represent its instant data rate fluctuation and that we can infer data rate characteristics of different video resolutions from a single given one. Based on our findings, we design active measurements for measuring the performance of Internet video.
 In this work, we perform a controlled study on the perceived experience of viewing YouTube videos as observed from the end users' point of view through their residential ISPs in a metropolitan area. This study is conducted using our tool Pytomo, which we developed to emulate the end users' experience of viewing YouTube videos. Pytomo crawls and downloads YouTube videos to collect a number of measures, including information about the YouTube servers that are delivering them. This open-source tool was provided to a group of volunteers located in the Kansas City metropolitan area. These volunteers, who use different residential ISPs to access the Internet, were instructed to synchronously run the tool to collect the measurement data. Based on the data collected over specific time windows (separated by three months), we observed that there is a noticeable difference in the quality of experience depending on the residential ISPs. Furthermore, the content distribution policies for YouTube, for different residential ISPs vary and the round trip time is not the primary factor for choosing video servers. This paper presents an in-depth study of YouTube video service delivery. We have designed a tool that crawls YouTube videos in order to precisely evaluate the quality of experience (QoE) as perceived by the user. We enrich the main QoE metric, the number of video stalls, with many network measurements and use multiple DNS servers to understand the main factors that impact QoS and QoE. This tool has been used in multiple configurations: first, to understand the main delivery policies of YouTube videos, then to understand the impact of the ISP on these policies and finally, to compare the US and Europe YouTube policies. Our main results are that: (i) geographical proximity does not matter inside Europe or the US, but link cost and ISP-dependent policies do; (ii) usual QoS metrics (RTT) have no impact on QoE (video stall); (iii) QoE is not impacted nowadays (with good access networks) by access capacity but by peering agreements between ISPs and CDNs, and by server load. We also indicate a network monitoring metric that can be used by ISPs to roughly evaluate the QoE of HTTP video streaming of a large set of clients at a reduced computational cost. YouTube, the killer application of today's Internet, is changing the way ISPs and network operators manage quality monitoring and provisioning on their IP networks. YouTube is currently the most consumed Internet application, accounting for more than 30 of the overall Internet's traffic worldwide. Coupling such an overwhelming traffic volume with the ever intensifying competition among ISPs is pushing operators to integrate Quality of Experience (QoE) paradigms into their traffic management systems. The need for automatic QoE assessment solutions becomes even more critical in mobile broadband networks, where over-provisioning solutions can not be foreseen and bad user experience translates into churning clients. This paper presents a complete study on the problem of YouTube Quality of Experience monitoring and assessment in mobile networks. The paper considers not only the QoE analysis, modeling and assessment based on real users' experience, but also the passive monitoring of the quality provided by the ISP to its end-customers in a large mobile broadband network.