  Mobile cloud and edge computing protocols make it possible to offer
computationally heavy applications to mobile devices via computational
offloading from devices to nearby edge servers or more powerful, but remote,
cloud servers. Previous work assumed that computational tasks can be
fractionally offloaded at both cloud processor (CP) and at a local edge node
(EN) within a conventional Distributed Radio Access Network (D-RAN) that relies
on non-cooperative ENs equipped with one-way uplink fronthaul connection to the
cloud. In this paper, we propose to integrate collaborative fractional
computing across CP and ENs within a Cloud RAN (C-RAN) architecture with
finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a
mobile device can be partially carried out at an EN and the CP, with multiple
ENs communicating with a common CP to exchange data and computational outcomes
while allowing for centralized precoding and decoding. Unlike prior work, we
investigate joint optimization of computing and communication resources,
including wireless and fronthaul segments, to minimize the end-to-end latency
by accounting for a two-way uplink and downlink transmission. The problem is
tackled by using fractional programming (FP) and matrix FP. Extensive numerical
results validate the performance gain of the proposed architecture as compared
to the previously studied D-RAN solution.
