  With the advancement in personal smart devices and pervasive network
connectivity, users are no longer passive content consumers, but also
contributors in producing new contents. This expansion in live services
requires a detailed analysis of broadcasters' and viewers' behavior to maximize
users' Quality of Experience (QoE). In this paper, we present a dataset
gathered from one of the popular live streaming platforms: Facebook. In this
dataset, we stored more than 1,500,000 live stream records collected in June
and July 2018. These data include public live videos from all over the world.
However, Facebook live API does not offer the possibility to collect online
videos with their fine grained data. The API allows to get the general data of
a stream, only if we know its ID (identifier). Therefore, using the live map
website provided by Facebook and showing the locations of online streams and
locations of viewers, we extracted video IDs and different coordinates along
with general metadata. Then, having these IDs and using the API, we can collect
the fine grained metadata of public videos that might be useful for the
research community. We also present several preliminary analyses to describe
and identify the patterns of the streams and viewers. Such fine grained details
will enable the multimedia community to recreate real-world scenarios
particularly for resource allocation, caching, computation, and transcoding in
edge networks. Existing datasets do not provide the locations of the viewers,
which limits the efforts made to allocate the multimedia resources as close as
possible to viewers and to offer better QoE.
