  Unmanned aerial vehicles (UAVs) are envisioned to provide diverse services
from the air. The service quality may rely on the wireless performance which is
affected by the UAV's position. In this paper, we focus on the UAV placement
problem in the Internet of Vehicles, where the UAV is deployed to monitor the
road traffic and sends the monitored videos to vehicles. The studied problem is
formulated as video resolution maximization by optimizing over the UAV's
position. Moreover, we take into account the maximal transmission delay and
impose a probabilistic constraint. To solve the formulated problem, we first
leverage the techniques in extreme value theory (EVT) and Gaussian process
regression (GPR) to characterize the influence of the UAV's position on the
delay performance. Based on this characterization, we subsequently propose a
proactive resolution selection and UAV placement approach, which adaptively
places the UAV according to the geographic distribution of vehicles. Numerical
results justify the joint usage of EVT and GPR for maximal delay
characterization. Through investigating the maximal transmission delay, the
proposed approach nearly achieves the optimal performance when vehicles are
evenly distributed, and reduces 10% and 19% of the 999-th 1000-quantile over
two baselines when vehicles are biased distributed.
