  Vehicles with autonomous driving capabilities are present on public streets.
However, edge cases remain that still require a human in-vehicle driver.
Assuming the vehicle manages to come to a safe state in an automated fashion,
teleoperated driving technology enables a human to resolve the situation
remotely by a control interface connected via a mobile network. While this is a
promising solution, it also introduces technical challenges, one of them being
the necessity to transmit video data of multiple cameras from the vehicle to
the human operator. In this paper, an adaptive video streaming framework
specifically designed for teleoperated driving is proposed and demonstrated.
The framework enables automatic reconfiguration of the video streams of the
multi-camera system at runtime. Predictions of variable transmission service
quality are taken into account. With the objective to maximize visual quality,
the framework uses so-called rate-quality models to dynamically allocate
bitrates and select resolution scaling factors. Results from deploying the
proposed framework on an actual teleoperated driving system are presented.
