TY - GEN
T1 - FacebookVideoLive18
T2 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
AU - Baccour, Emna
AU - Erbad, Aiman
AU - Bilal, Kashif
AU - Mohamed, Amr
AU - Guizani, Mohsen
AU - Hamdi, Mounir
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - 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 realworld 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.
AB - 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 realworld 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.
KW - Facebook live
KW - Live video streaming dataset
KW - cloud
KW - edge computing
KW - resource allocation
KW - streams pattern
KW - viewers pattern
UR - http://www.scopus.com/inward/record.url?scp=85085507006&partnerID=8YFLogxK
U2 - 10.1109/ICIoT48696.2020.9089607
DO - 10.1109/ICIoT48696.2020.9089607
M3 - Conference contribution
AN - SCOPUS:85085507006
T3 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
SP - 476
EP - 483
BT - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 February 2020 through 5 February 2020
ER -