  Mobile edge computing (MEC) provides low-latency offloading solutions for
computationally intensive tasks, effectively improving the computing efficiency
and battery life of mobile devices. However, for data-intensive tasks or
scenarios with limited uplink bandwidth, network congestion might occur due to
massive simultaneous offloading nodes, increasing transmission latency and
affecting task performance. In this paper, we propose a semantic-aware
multi-modal task offloading framework to address the challenges posed by
limited uplink bandwidth. By introducing a semantic extraction factor, we
balance the relationship among transmission latency, computation energy
consumption, and task performance. To measure the offloading performance of
multi-modal tasks, we design a unified and fair quality of experience (QoE)
metric that includes execution latency, energy consumption, and task
performance. Lastly, we formulate the optimization problem as a Markov decision
process (MDP) and exploit the multi-agent proximal policy optimization (MAPPO)
reinforcement learning algorithm to jointly optimize the semantic extraction
factor, communication resources, and computing resources to maximize overall
QoE. Experimental results show that the proposed method achieves a reduction in
execution latency and energy consumption of 18.1% and 12.9%, respectively
compared with the semantic-unaware approach. Moreover, the proposed approach
can be easily extended to models with different user preferences.
