  This paper investigates the intelligent computing task-oriented computing
offloading and semantic compression in mobile edge computing (MEC) systems.
With the popularity of intelligent applications in various industries,
terminals increasingly need to offload intelligent computing tasks with complex
demands to MEC servers for computing, which is a great challenge for bandwidth
and computing capacity allocation in MEC systems. Considering the accuracy
requirement of intelligent computing tasks, we formulate an optimization
problem of computing offloading and semantic compression. We jointly optimize
the system utility which are represented as computing accuracy and task delay
respectively to acquire the optimized system utility. To solve the proposed
optimization problem, we decompose it into computing capacity allocation
subproblem and compression offloading subproblem and obtain solutions through
convex optimization and successive convex approximation. After that, the
offloading decisions, computing capacity and compressed ratio are obtained in
closed forms. We design the computing offloading and semantic compression
algorithm for intelligent computing tasks in MEC systems then. Simulation
results represent that our algorithm converges quickly and acquires better
performance and resource utilization efficiency through the trend with total
number of users and computing capacity compared with benchmarks.
