  Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to
automate the information creation process while fulfilling the personalized
requirements of users. However, due to the instability of AIGC models, e.g.,
the stochastic nature of diffusion models, the quality and accuracy of the
generated content can vary significantly. In wireless edge networks, the
transmission of incorrectly generated content may unnecessarily consume network
resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is
required to enable users to connect to the most suited ASP, improving the
users' satisfaction and quality of generated content. In this article, we first
review the AIGC techniques and their applications in wireless networks. We then
present the AIGC-as-a-service (AaaS) concept and discuss the challenges in
deploying AaaS at the edge networks. Yet, it is essential to have performance
metrics to evaluate the accuracy of AIGC services. Thus, we introduce several
image-based perceived quality evaluation metrics. Then, we propose a general
and effective model to illustrate the relationship between computational
resources and user-perceived quality evaluation metrics. To achieve efficient
AaaS and maximize the quality of generated content in wireless edge networks,
we propose a deep reinforcement learning-enabled algorithm for optimal ASP
selection. Simulation results show that the proposed algorithm can provide a
higher quality of generated content to users and achieve fewer crashed tasks by
comparing with four benchmarks, i.e., overloading-avoidance, random,
round-robin policies, and the upper-bound schemes.
