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 @cite_22 , the authors use over 20 million randomly selected YouTube videos to show that the popularity of videos is constrained by geographical locations. Our methodology is in line with this as we gathered all available location-based charts from YouTube, giving our dataset regional representation. Furthermore, our proposal to LMAP for testing video streaming also recommends using location-based charts for measuring user experience.
@cite_22
2080318890 1640490335 1986697120 2056021151
One of the most popular user activities on the Web is watching videos. Services like YouTube, Vimeo, and Hulu host and stream millions of videos, providing content that is on par with TV. While some of this content is popular all over the globe, some videos might be only watched in a confined, local region. In this work we study the relationship between popularity and locality of online YouTube videos. We investigate whether YouTube videos exhibit geographic locality of interest, with views arising from a confined spatial area rather than from a global one. Our analysis is done on a corpus of more than 20 millions YouTube videos, uploaded over one year from different regions. We find that about 50 of the videos have more than 70 of their views in a single region. By relating locality to viralness we show that social sharing generally widens the geographic reach of a video. If, however, a video cannot carry its social impulse over to other means of discovery, it gets stuck in a more confined geographic region. Finally, we analyze how the geographic properties of a video's views evolve on a daily basis during its lifetime, providing new insights on how the geographic reach of a video changes as its popularity peaks and then fades away. Our results demonstrate how, despite the global nature of the Web, online video consumption appears constrained by geographic locality of interest: this has a potential impact on a wide range of systems and applications, spanning from delivery networks to recommendation and discovery engines, providing new directions for future research. 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 a traffic characterization study of the popular video sharing service, YouTube. Over a three month period we observed almost 25 million transactions between users on an edge network and YouTube, including more than 600,000 video downloads. We also monitored the globally popular videos over this period of time. In the paper we examine usage patterns, file properties, popularity and referencing characteristics, and transfer behaviors of YouTube, and compare them to traditional Web and media streaming workload characteristics. We conclude the paper with a discussion of the implications of the observed characteristics. For example, we find that as with the traditional Web, caching could improve the end user experience, reduce network bandwidth consumption, and reduce the load on YouTube's core server infrastructure. Unlike traditional Web caching, Web 2.0 provides additional meta-data that should be exploited to improve the effectiveness of strategies like caching. The rapid growth of the number of videos in YouTube provides enormous potential for users to find content of interest to them. Unfortunately, given the difficulty of searching videos, the size of the video repository also makes the discovery of new content a daunting task. In this paper, we present a novel method based upon the analysis of the entire user-video graph to provide personalized video suggestions for users. The resulting algorithm, termed Adsorption, provides a simple method to efficiently propagate preference information through a variety of graphs. We extensively test the results of the recommendations on a three month snapshot of live data from YouTube.