  The explosive development of the Internet of Things (IoT) has led to
increased interest in mobile edge computing (MEC), which provides computational
resources at network edges to accommodate computation-intensive and
latency-sensitive applications. Intelligent reflecting surfaces (IRSs) have
gained attention as a solution to overcome blockage problems during the
offloading uplink transmission in MEC systems. This paper explores IRS-aided
multi-cell networks that enable servers to serve neighboring cells and
cooperate to handle resource exhaustion. We aim to minimize the joint energy
and latency cost, by jointly optimizing computation tasks, edge computing
resources, user beamforming, and IRS phase shifts. The problem is decomposed
into two subproblems--the MEC subproblem and the IRS communication
subproblem--using the block coordinate descent (BCD) technique. The MEC
subproblem is reformulated as a nonconvex quadratic constrained problem (QCP),
while the IRS communication subproblem is transformed into a weight-sum-rate
problem with auxiliary variables. We propose an efficient algorithm to
iteratively optimize MEC resources and IRS communication until convergence.
Numerical results show that our algorithm outperforms benchmarks and that
multi-cell MEC systems achieve additional performance gains when supported by
IRS.
