  Efficient data offloading plays a pivotal role in computational-intensive
platforms as data rate over wireless channels is fundamentally limited. On top
of that, high mobility adds an extra burden in vehicular edge networks (VENs),
bolstering the desire for efficient user-centric solutions. Therefore, unlike
the legacy inflexible network-centric approach, this paper exploits a
software-defined flexible, open, and programmable networking platform for an
efficient user-centric, fast, reliable, and deadline-constrained offloading
solution in VENs. In the proposed model, each active vehicle user (VU) is
served from multiple low-powered access points (APs) by creating a noble
virtual cell (VC). A joint node association, power allocation, and distributed
resource allocation problem is formulated. As centralized learning is not
practical in many real-world problems, following the distributed nature of
autonomous VUs, each VU is considered an edge learning agent. To that end,
considering practical location-aware node associations, a joint radio and power
resource allocation non-cooperative stochastic game is formulated. Leveraging
reinforcement learning's (RL) efficacy, a multi-agent RL (MARL) solution is
proposed where the edge learners aim to learn the Nash equilibrium (NE)
strategies to solve the game efficiently. Besides, real-world map data, with a
practical microscopic mobility model, are used for the simulation. Results
suggest that the proposed user-centric approach can deliver remarkable
performances in VENs. Moreover, the proposed MARL solution delivers
near-optimal performances with approximately 3% collision probabilities in case
of distributed random access in the uplink.
