  Mobile Edge Computing (MEC) enables rich services in close proximity to the
end users to provide high quality of experience (QoE) and contributes to energy
conservation compared with local computing, but results in increased
communication latency. In this paper, we investigate how to jointly optimize
task offloading and resource allocation to minimize the energy consumption in
an orthogonal frequency division multiple access-based MEC networks, where the
time-sensitive tasks can be processed at both local users and MEC server via
partial offloading. Since the optimization variables of the problem are
strongly coupled, we first decompose the orignal problem into three subproblems
named as offloading selection (PO ), transmission power optimization (PT ), and
subcarriers and computing resource allocation (PS ), and then propose an
iterative algorithm to deal with them in a sequence. To be specific, we derive
the closed-form solution for PO , employ the equivalent parametric convex
programming to cope with the objective function which is in the form of sum of
ratios in PT , and deal with PS by an alternating way in the dual domain due to
its NP-hardness. Simulation results demonstrate that the proposed algorithm
outperforms the existing schemes.
