A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered
  by Semantic Communication
Runze Cheng Yao Sun Dusit Niyato Lan Zhang Lei Zhang Muhammad Ali Imran
Generative AI applications are 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 for AIGC products. However, it is
challenging to provide optimal 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 extracted and transmitted by using SemCom. Specifically, SemAIGC integrates
diffusion-based models within the semantic encoder and decoder for efficient
content generation and flexible adjustment of the computing workload of both
transmitter and receiver. Meanwhile, we devise a resource-aware workload
trade-off (ROOT) scheme into the SemAIGC framework to intelligently decide
transmitter/receiver workload, thus adjusting the utilization of computational
resource according to service requirements. Simulations verify the superiority
of our proposed SemAIGC framework in terms of latency and content quality
compared to conventional approaches.