  Consider a multi-cell mobile edge computing network, in which each user
wishes to compute the product of a user-generated data matrix with a
network-stored matrix. This is done through task offloading by means of input
uploading, distributed computing at edge nodes (ENs), and output downloading.
Task offloading may suffer long delay since servers at some ENs may be
straggling due to random computation time, and wireless channels may experience
severe fading and interference. This paper aims to investigate the interplay
among upload, computation, and download latencies during the offloading process
in the high signal-to-noise ratio regime from an information-theoretic
perspective. A policy based on cascaded coded computing and on coordinated and
cooperative interference management in uplink and downlink is proposed and
proved to be approximately optimal for a sufficiently large upload time. By
investing more time in uplink transmission, the policy creates data redundancy
at the ENs, which can reduce the computation time, by enabling the use of coded
computing, as well as the download time via transmitter cooperation. Moreover,
the policy allows computation time to be traded for download time. Numerical
examples demonstrate that the proposed policy can improve over existing schemes
by significantly reducing the end-to-end execution time.
