  A conception of mobile edge generation (MEG) is proposed, where generative
artificial intelligence (GAI) models are distributed at edge servers (ESs) and
user equipment (UE), enabling joint execution of generation tasks. Various
distributed deployment schemes of the GAI model are proposed to alleviate the
immense network load and long user queuing times for accessing GAI models. Two
MEG frameworks are proposed, namely the single-ES framework and the multi-ESs
framework. 1) A one-to-one joint generation framework between an ES and a UE is
proposed, including four specific single-ES MEG protocols. These protocols
allow distributed GAI models to transmit seeds or sketches for delivering
information efficiently. 2) Several protocols are proposed for multi-ESs MEG,
which enable multiple ESs to perform the generation task cooperatively or in
parallel. Finally, a case study of a text-guided-image-to-image generation is
provided, where a latent diffusion model is distributed at an ES and a UE. The
simulation results demonstrate that the proposed protocols are able to generate
high-quality images at extremely low signal-to-noise ratios. The proposed
protocols can significantly reduce the communication overhead compared to the
centralized model.
