  This paper studies a multi-user cooperative mobile-edge computing (MEC)
system, in which a local mobile user can offload intensive computation tasks to
multiple nearby edge devices serving as helpers for remote execution. We focus
on the scenario where the local user has a number of independent tasks that can
be executed in parallel but cannot be further partitioned. We consider a time
division multiple access (TDMA) communication protocol, in which the local user
can offload computation tasks to the helpers and download results from them
over pre-scheduled time slots. Under this setup, we minimize the local user's
computation latency by optimizing the task assignment jointly with the time and
power allocations, subject to individual energy constraints at the local user
and the helpers. However, the joint task assignment and wireless resource
allocation problem is a mixed-integer non-linear program (MINLP) that is hard
to solve optimally. To tackle this challenge, we first relax it into a convex
problem, and then propose an efficient suboptimal solution based on the optimal
solution to the relaxed convex problem. Finally, numerical results show that
our proposed joint design significantly reduces the local user's computation
latency, as compared against other benchmark schemes that design the task
assignment separately from the offloading/downloading resource allocations and
local execution.
