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Generative AI applications are recently catering to a vast user base bycreating diverse and high-quality AI-generated content (AIGC). With theproliferation of mobile devices and rapid growth of mobile traffic, providingubiquitous access to high-quality AIGC services via wireless communicationnetworks is becoming the future direction for AIGC products. However, it ischallenging to provide optimal AIGC services in wireless networks with unstablechannels, limited bandwidth resources, and unevenly distributed computationalresources. To tackle these challenges, we propose a semantic communication(SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, whereonly semantic information of the content rather than all the binary bits shouldbe extracted and transmitted by using SemCom. Specifically, SemAIGC integratesdiffusion-based models within the semantic encoder and decoder for efficientcontent generation and flexible adjustment of the computing workload of bothtransmitter and receiver. Meanwhile, we devise a resource-aware workloadtrade-off (ROOT) scheme into the SemAIGC framework to intelligently decidetransmitter/receiver workload, thus adjusting the utilization of computationalresource according to service requirements. Simulations verify the superiorityof our proposed SemAIGC framework in terms of latency and content qualitycompared to conventional approaches.

A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication