  Mobile device users can now easily capture and socially share video clips in
a timely manner by uploading them wirelessly to a server. When attending
crowded events, such as an exhibition or the Olympic Games, however, timely
sharing of videos becomes difficult due to choking bandwidth in the network
infrastructure, preventing like-minded attendees from easily sharing videos
with each other through a server. One solution to alleviate this problem is to
use direct device-to-device communication to share videos among nearby
attendees. Contact capacity between two devices, however, is limited, and thus
a recommendation algorithm, such as collaborative filtering, is needed to
select and transmit only videos of potential interest to an attendee. In this
paper, we address the question: which video clip should be transmitted to which
user. We proposed an video transmission scheduling algorithm, called CoFiGel,
that runs in a distributed manner and aims to improve both the prediction
coverage and precision of the collaborative filtering algorithm. At each
device, CoFiGel transmits the video that would increase the estimated number of
positive user-video ratings the most if this video is transferred to the
destination device. We evaluated CoFiGel using real-world traces and show that
substantial improvement can be achieved compared to baseline schemes that do
not consider rating or contact history.
