  Multi-Access edge computing (MEC) is an emerging paradigm where users offload
computationally intensive jobs to the Access Point (AP). Given that the AP's
resources are shared by selfish users, pricing is a useful tool for
incentivising users to internalize the negative externality of delay they cause
to other users. Nevertheless, different users have different negative
valuations towards delay as some are more delay sensitive. To serve
heterogeneous users, we propose a priority pricing scheme where users can get
served first for a higher price. Our goal is to find the prices such that in
decision making, users will choose the class and the offloading frequency that
jointly maximize social welfare. With the assumption that the AP knows users'
profit functions, we derive in semi-closed form the optimal prices. However in
practice, the reporting of users's profit information incurs a large signalling
overhead. Besides, in reality users might falsely report their private profit
information. To overcome this, we further propose a learning-based pricing
mechanism where no knowledge of individual user profit functions is required.
At equilibrium, the optimal prices and average edge delays are learnt, and
users have chosen the correct priority class and offload at the socially
optimal frequency.
