An analysis of the YouNow live streaming platform

Abstract

Video streaming platforms like Twitch.tv or YouNow have attracted the attention of both users and researchers in the last few years. Users increasingly adopt these platforms to share user-generated videos while researchers study their usage patterns to learn how to provide better and new services.In this paper, we focus on the YouNow platform and show the results of an analysis of its traffic patterns and other character istics. To perform this analysis, we have collected YouNow usage patterns for 85994 users over a period of about one month.Our results show that YouNow's characteristics are in part equal to and in part different from those of other video streaming platforms. Like on You Tube or Twitch.tv, for instance, few YouNow videos attract most of the view requests. On the other side, YouNow sessions are notably shorter than Twitch.tv ones.We believe the observation of these similarities and differences to be crucial to inform the design and implementation of better upcoming video streaming services.

Exploring the Emerging Domain of Research on Video Game Live Streaming in Web of Science: State of the Art, Changes and Trends

“…A substantial number of these papers have their roots in the discipline of psychology, which has a long tradition of developing and using models to explain behavior [ 59 , 60 , 65 ]. The subdomains focused on the platform and the transmission are characterized by the use of more heterogeneous methods, although the former stands out for the inclusion of descriptive statistics and techniques for constructing graphs [ 87 , 88 , 89 , 90 , 91 , 92 ], and the latter for compiling information through application programming interfaces (APIs), both for Twitch [ 73 , 93 , 94 , 95 , 96 , 97 ] and other platforms such as Douyu [ 98 ]. The collection of information through public APIs is spreading to other subdomains (Streamer [ 99 ] Riot Games API; Receiver [ 100 ] Twitch API; and Platform [ 87 , 89 , 91 ] Twitch API), opening up future research opportunities if simultaneous information about the streamer, receiver, and platform can be connected.…”

“…In Figure 3 , it can be seen how the number of documents related to video game live streaming started to increase in 2015, although they were still mostly from conferences [ 87 , 88 , 103 , 110 , 132 , 133 ]. Among them were the first analyses of specific platforms such as Twitch [ 87 , 110 , 132 ] or YouNow [ 88 ]. The predominant research area is still computer science.…”

What Drives Streamers? Users’ Characteristics and Motivations on Social Live Streaming Services

“…Apart from surveys, interviews, and experiments in controlled situations, we are able to use two different sources for data gathering on SLSSs, namely log-files of the information systems, and systematic observations of the online videos. As logfiles' data are not very meaningful (it is impossible to get data on users' motives and streams' content) we can only use this source for some basic data as, for instance, for describing some user characteristics (e.g., country of dial-up) and interaction data (e.g., time on the SLSS) [36]. We realized systematic observations of the live streams.…”

Bitrate Adaptation for Video Streaming Services in Edge Caching Systems

“…q max . The range of M τ implies that the storage size can support upto I − 1 streamers with its highest available bitrate q max and video bitrate q max −1 for the last streamer without violating constraint (4). In this scenario, for each streamer except the last streamer, it costs (q max − 1) iterations to upgrade video bitrate from level 1 to q max .…”