Background

Table of Links

Abstract and 1. Introduction

1.1 Background

1.2 Motivation

1.3 Our Work and Contributions and 1.4 Organization

2. Related Work

   2.1 Mobile AIGC and Its QoE Modeling

   2.2 Blockchain for Mobile Networks

3. Preliminaries

4. Prosecutor Design

   4.1 Architecture Overview

   4.2 Reputation Roll-up

   4.3 Duplex Transfer Channel

5. OS2a: Objective Service Assessment for Mobile AIGC

   5.1 Inspiration from DCM

   5.2 Objective Quality of the Service Process

   5.3 Subjective Experience of AIGC Outputs

6. OS2A on Prosecutor: Two-Phase Interaction for Mobile AIGC

   6.1 MASP Selection by Reputation

   6.2 Contract Theoretic Payment Scheme

7. Implementation and Evaluation

   7.1 Implementation and Experimental Setup

   7.2 Prosecutor Performance Evaluation

   7.3 Investigation of Functional Goals

   7.4 Security Analysis

8. Conclusion and References

1.1 Background

With the deepening of AIGC applications, the scalability concern is eminently exposed. Currently, most AIGC services rely on large pre-trained models with billions of parameters, consuming considerable storage and computation resources. For instance, running Stable Diffusion requires at least one NVIDIA Ampere GPU with 6 GB memory [1], which is unaffordable for many resource-constrained clients [3]. To this end, researchers recently presented the concept of Mobile AIGC and successfully developed a series of ondevice AIGC models, e.g., MediaPipe and PaLM 2-Gecko by Google [4] and the lightweight Stable Diffusion by Chen et al. [5]. In the mobile AIGC era, clients can request AIGC inferences from Mobile AIGC Service Providers (MASPs) [6]. Since MASPs are close to clients, low service latency can be realized. Additionally, clients are able to customize the AIGC services, e.g., sharing real-time background information with MASPs to render immersive 3D environments. Furthermore, the network-wide resources and service requests can be provisioned, forming the AIGC-as-a-Service paradigm [7]. Despite these advantages, the interactions between clients and MASPs in mobile AIGC are complicated, pertaining to the following mechanisms.

• MASP Selection: In mobile AIGC, each client can access multiple nearby MASPs with varying computing power, capability, and reliability. Hence, the MASP selection mechanism should incorporate these factors and select the best MASP with the highest probability of meeting the client’s service requirements.

• Payment Scheme: Afterward, the client confirms service details with the selected MASP. A payment scheme is required, which specifies the payment method (e.g., pre-paid or post-paid) and the amount of the service fee (e.g., fixed or floating value) according to the service quality promised by the MASP.

• Fee-Ownership Transfer: Once finishing the AIGC inferences, a transfer mechanism should be employed. In this way, the client and MASP can transfer the service fee and the ownership of the AIGC output to each other in a secure manner.