YouTube live and Twitch

TLDR

User‑generated live video streaming services, such as Twitch and YouTube Live, allow anyone to broadcast over the Internet and have become popular competitors to traditional cable TV, especially in e‑sports. This paper introduces a dataset to support further research on these live streaming systems. The dataset comprises three months of traces (January–April 2014) sampled every five minutes, recording broadcaster identifiers, viewer counts, and media metadata for Twitch and YouTube Live. Analysis reveals that both platforms generate traffic with peaks exceeding 1 Tbps, Twitch hosts over a million uploaders providing continuous service, and channel popularity is more heterogeneous than in other user‑generated content services.

Abstract

User-Generated live video streaming systems are services that allow anybody to broadcast a video stream over the Internet. These Over-The-Top services have recently gained popularity, in particular with e-sport, and can now be seen as competitors of the traditional cable TV. In this paper, we present a dataset for further works on these systems. This dataset contains data on the two main user-generated live streaming systems: Twitch and the live service of YouTube. We got three months of traces of these services from January to April 2014. Our dataset includes, at every five minutes, the identifier of the online broadcaster, the number of people watching the stream, and various other media information. In this paper, we introduce the dataset and we make a preliminary study to show the size of the dataset and its potentials. We first show that both systems generate a significant traffic with frequent peaks at more than 1 Tbps. Thanks to more than a million unique uploaders, Twitch is in particular able to offer a rich service at anytime. Our second main observation is that the popularity of these channels is more heterogeneous than what have been observed in other services gathering user-generated content.