  To accommodate exponentially increasing traffic demands of vehicle-based
applications, operators are utilizing offloading as a promising technique to
improve quality of service (QoS), which gives rise to the application of Mobile
Edge Computing (MEC). While the conventional offloading paradigms focus on
delay and energy tradeoff, they either fail to find efficient models to
represent delay, especially the queueing delay, or underestimate the role of
MEC Server. In this paper, we propose a novel \textbf{P}re-\textbf{A}llocation
\textbf{D}esign for vehicular \textbf{O}ffloading (\textbf{PADO}). A task delay
queue is constructed based on an allocate-execute separate (AES) mechanism. Due
to the dynamics of vehicular network, we are inspired to utilize Lyapunov
optimization to minimize the execution cost of each vehicle and guarantee task
delay. The MEC Server with energy harvesting devices is also taken into
consideration of the system. The transaction between vehicles and server is
decided by a Stackelberg Game framework. We conduct extensive experiments to
show the property and superiority of our proposed framework.
