  By offloading intensive computation tasks to the edge cloud located at the
cellular base stations, mobile-edge computation offloading (MECO) has been
regarded as a promising means to accomplish the ambitious millisecond-scale
end-to-end latency requirement of the fifth-generation networks. In this paper,
we investigate the latency-minimization problem in a multi-user time-division
multiple access MECO system with joint communication and computation resource
allocation. Three different computation models are studied, i.e., local
compression, edge cloud compression, and partial compression offloading. First,
closed-form expressions of optimal resource allocation and minimum system delay
for both local and edge cloud compression models are derived. Then, for the
partial compression offloading model, we formulate a piecewise optimization
problem and prove that the optimal data segmentation strategy has a piecewise
structure. Based on this result, an optimal joint communication and computation
resource allocation algorithm is developed. To gain more insights, we also
analyze a specific scenario where communication resource is adequate while
computation resource is limited. In this special case, the closed-form solution
of the piecewise optimization problem can be derived. Our proposed algorithms
are finally verified by numerical results, which show that the novel partial
compression offloading model can significantly reduce the end-to-end latency.
