Cross-Modal Generative Semantic Communications for Mobile AIGC: Joint Semantic Encoding and Prompt Engineering

TL;DR

This paper introduces a cross-modal semantic communication framework for mobile AI-generated content that significantly reduces bandwidth usage by transmitting only essential semantic information, while maintaining high output quality.

Contribution

It proposes a joint semantic encoding and prompt engineering approach, along with a novel attention-aware diffusion algorithm, to optimize bandwidth and enhance content recovery in mobile AIGC services.

Findings

Bandwidth consumption reduced by 49.4% on average.

ADD algorithm outperforms baseline methods with 1.74x higher reward.

High perceptual quality maintained despite reduced data transmission.

Abstract

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services can become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modal Generative Semantic Communications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize a series of cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perpetual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability. Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74x higher overall reward.