  Self-driving vehicles are expected to bring many benefits among which
enhancing traffic efficiency and relia-bility, and reducing fuel consumption
which would have a great economical and environmental impact. The success of
this technology heavily relies on the full situational awareness of its
surrounding entities. This is achievable only when everything is networked,
including vehicles, users and infrastructure, and exchange the sensed data
among the nearby objects to increase their awareness. Nevertheless, human
intervention is still needed in the loop anyway to deal with unseen situations
or compensate for inaccurate or improper vehicle's decisions. For such cases,
video feed, in addition to other data such as LIDAR, is considered essential to
provide humans with the real picture of what is hap-pening to eventually take
the right decision. However, if the video is not delivered in a timely
fashion,it becomes useless or likely produce catastrophic outcomes.
Additionally, any disruption in the streamed video, for instance during
handover operation while traversing inter-countries cross borders, is very
annoying to the user and possibly ause damages as well. In this article, we
start by describing two important use cases, namely Remote Driving and
Platooning, where the timely delivery of video is of extreme importance [1].
Thereafter, we detail our implemented solution to accommodate the
aforementioned use cases for self-driving vehicles. Through extensive
experiments in local and LTE networks, we show that our solution ensures a very
low end-to-end latency. Also, we show that our solution keeps the video outage
as low as possible during handover operation.
