  By offering shared computational facilities to which mobile devices can
offload their computational tasks, the mobile edge computing framework is
expanding the scope of applications that can be provided on
resource-constrained devices. When multiple devices seek to use such a facility
simultaneously, both the available computational resources and the available
communication resources need to be appropriately allocated. In this manuscript,
we seek insight into the impact of the choice of the multiple access scheme by
developing solutions to the mobile energy minimization problem in the two-user
case with plentiful shared computational resources. In that setting, the
allocation of communication resources is constrained by the latency constraints
of the applications, the computational capabilities and the transmission power
constraints of the devices, and the achievable rate region of the chosen
multiple access scheme. For both indivisible tasks and the limiting case of
tasks that can be infinitesimally partitioned, we provide a closed-form and
quasi-closed-form solution, respectively, for systems that can exploit the full
capabilities of the multiple access channel, and for systems based on
time-division multiple access (TDMA). For indivisible tasks, we also provide
quasi-closed-form solutions for systems that employ sequential decoding without
time sharing or independent decoding. Analyses of our results show that when
the channel gains are equal and the transmission power budgets are larger than
a threshold, TDMA (and the suboptimal multiple access schemes that we have
considered) can achieve an optimal solution. However, when the channel gains of
each user are significantly different and the latency constraints are tight,
systems that take advantage of the full capabilities of the multiple access
channel can substantially reduce the energy required to offload.
