  Edge computing technology has great potential to improve various
computation-intensive applications in vehicular networks by providing
sufficient computation resources for vehicles. However, it is still a challenge
to fully unleash the potential of edge computing in edge computing-enabled
vehicular networks. In this paper, we develop the energy-efficient cooperative
offloading scheme for edge computing-enabled vehicular networks, which splits
the task into multiple subtasks and offloads them to different roadside units
(RSUs) located ahead along the route of the vehicle. We first establish novel
cooperative offloading models for the offline and online scenarios in edge
computing-enabled vehicular networks. In each offloading scenario, we formulate
the total energy minimization with respect to the task splitting ratio,
computation resource, and communication resource. In the offline scenario, we
equivalently transform the original problem to a convex problem and obtain
optimal solutions for multi-vehicle case and single-vehicle case, respectively.
Furthermore, we show that the method proposed for the offline scenario can also
be applied to solve the optimization problem in the online scenario. Finally,
through numerical results, by analyzing the impact of network parameters on the
total energy consumption, we verify that our proposed solution consumes lower
energy than baseline schemes.
