  Generative AI applications have been recently catering to a vast user base by
creating diverse and high-quality AI-generated content (AIGC). With the
proliferation of mobile devices and rapid growth of mobile traffic, providing
ubiquitous access to high-quality AIGC services via wireless communication
networks is becoming the future direction. However, it is challenging to
provide qualified AIGC services in wireless networks with unstable channels,
limited bandwidth resources, and unevenly distributed computational resources.
To tackle these challenges, we propose a semantic communication
(SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where
only semantic information of the content rather than all the binary bits should
be generated and transmitted by using SemCom. Specifically, SemAIGC integrates
diffusion models within the semantic encoder and decoder to design a
workload-adjustable transceiver thereby allowing adjustment of computational
resource utilization in edge and local. In addition, a Resource-aware wOrk lOad
Trade-off (ROOT) scheme is devised to intelligently make workload adaptation
decisions for the transceiver, thus efficiently generating, transmitting, and
fine-tuning content as per dynamic wireless channel conditions and service
requirements. Simulations verify the superiority of our proposed SemAIGC
framework in terms of latency and content quality compared to conventional
approaches.
