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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

Hongyang Du, Zonghang Li, Dusit Niyato, Jiawen Kang, Zehui Xiong, Xuemin, Shen, Dong In Kim

arXiv:2301.03220v1cs.AIcs.SYeess.SY

TL;DR

Wireless edge AIGC must handle unstable content quality, scarce resources, and diverse user tasks while selecting suitable service providers. The paper reviews AIGC and AaaS, models perceived quality against computational resources, and applies DRL to dynamic ASP selection. The proposed approach is reported to provide higher-quality content and fewer crashed tasks than four benchmark policies.

  • Problem

    Deploying AIGC in wireless edge networks requires selecting suitable ASPs despite variable quality, limited resources, diverse tasks, and overload risks.

  • Method

    The paper reviews AIGC and AaaS, models image-based perceived quality against computational resources, and uses SAC-based DRL for dynamic ASP selection.

  • Results

    The proposed DRL-enabled ASP selection achieves higher generated-content quality and fewer crashed tasks than upper-bound, overloading-avoidance, random, and round-robin policies.

  • Takeaways & Limitations

    Dynamic ASP selection can support efficient AaaS by combining content-quality awareness with avoidance of task crashes and congestion penalties.

Abstract

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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.

I. INTRODUCTION

The paper frames AaaS as a way to deliver personalized AIGC through wireless edge networks despite deployment, resource, and model-quality challenges. It introduces perceived-quality modeling and DRL-based ASP selection as solutions.

  • AIGC automates creation of text, images, and videos, saving time and resources otherwise spent on manual content creation.
  • Large training requirements, limited edge capacity, task-specific models, and variable model quality complicate deploying multiple AIGC services at every edge device.The paper motivates AaaS by placing models on edge servers for low-latency, customizable access.
  • AaaS deployment can provide personalization, efficiency, and flexibility, but assigning suitable ASPs must balance user quality against service overload and retransmissions.
  • The paper reviews image-based perceived-quality metrics and proposes a general model relating computational resource consumption to generated-content quality using real experiments.
  • A DRL-enabled method dynamically selects optimal ASPs and is reported superior to upper-bound, overloading-avoidance, random, and round-robin policies.

II. AI-GENERATED CONTENT AND TECHNIQUES

The paper surveys AIGC technologies used to generate content and situates them as foundations for applications in wireless edge networks.

  • II. AI-GENERATED CONTENT AND TECHNIQUES: The section introduces AIGC and its underlying generative techniques before discussing categories and edge-network applications.
  • A. Generative Techniques: Autoregressive models predict subsequent elements, while variational autoencoders learn latent representations for decoding synthetic data.
  • A. Generative Techniques: GANs jointly train generator and discriminator networks to improve realistic image generation and synthetic-image discrimination.
  • A. Generative Techniques: Flow-based models transform simple distributions into target distributions through invertible neural-network transformations.
  • A. Generative Techniques: Diffusion models learn to reverse Gaussian-noise corruption, and Transformer techniques are also discussed as AIGC model-building tools.

B. Categories of AIGC and Applications in Mobile Networks

The paper categorizes AIGC by input and output modality and connects these categories to wireless edge-network use cases.

  • B. Categories of AIGC and Applications in Mobile Networks: AIGC categories and their wireless edge-network applications are presented as potential directions for future research.
  • 1) Text-to-Text AIGC:: Text-to-text AIGC generates human-like messages from text inputs for answering, translation, summarization, and vehicle navigation services.
  • B. Categories of AIGC and Applications in Mobile Networks: Figure 1 organizes generative techniques, AIGC categories, and wireless edge-network applications, with representative services listed for each modality.
  • 2) Text-to-Image AIGC:: Text-to-image AIGC creates visual content from written descriptions, supporting vehicle path planning and user-directed art generation.

3) Text-to-3D AIGC:

The paper extends the modality survey to text-to-3D, image-to-image, and audio-related generation, then introduces AaaS as an edge-network deployment context.

  • 3) Text-to-3D AIGC:: Text-to-3D AIGC generates 3D models from text for wireless AR and next-generation services such as the Metaverse.
  • 4) Image-to-Image AIGC:: Image-to-image AIGC transforms source images into realistic or stylized outputs and supports sketch-based creation, editing, and image repair.
  • 5) Audio-related AIGC:: Audio-related AIGC analyzes and generates speech or music, including text-to-speech, music synthesis, audio-visual composition, and voice assistants.
  • III. AI-GENERATED CONTENT-AS-A-SERVICE IN WIRELESS EDGE NETWORKS: The paper next examines AaaS in wireless edge networks, where users request generated content from models hosted on edge servers and deployment challenges remain.

A. AI-Generated Content-as-a-Service and Challenges

AaaS deployments place trained AIGC models on edge servers, but must balance bandwidth, channel, dataset, computation, and incentive challenges while evaluating user-perceived quality.

  • AaaS hosts trained AIGC models on edge servers so users can submit generation requests and receive content from ASP-operated infrastructure.Models require continuous maintenance and updates to remain accurate and effective.
  • High-resolution AaaS can consume substantial bandwidth because uploads and downloads are large, while repeated requests may further load edge servers.An AI-generated wallpaper may be around 10 Megabytes.
  • Time-varying fading channels can degrade AaaS QoS through low SNR, low outage probability, and high bit-error probability.
  • ASP selection should account for training datasets because models trained on different data suit different generation tasks, such as avatar creation from face-image datasets.
  • Diffusion-model quality can improve with inference steps, but generating content still consumes computation and time.
  • Evaluating AIGC performance requires user-relevant metrics and a model linking computational consumption, such as inference steps, to generated-content quality.

B. Performance Metric Modelling

The paper focuses on image-based perceived-quality metrics for AaaS, distinguishing no-reference and full-reference assessment according to whether an original image is available.

  • B. Performance Metric Modelling: Image-quality assessment is divided into distribution-based and image-based metrics, with image-based methods emphasized because they better reflect subjective user quality in practical AaaS.
  • 1) Image-based metrics:: No-reference assessment is appropriate when the original image is unavailable, while full-reference methods apply when an AaaS reference image exists.
  • 1) Image-based metrics:: TV measures image smoothness or roughness, and BRISQUE uses locally normalized luminance statistics to quantify distortion-related losses of naturalness.Higher image quality corresponds to smaller TV and BRISQUE values.
  • 1) Image-based metrics:: Full-reference metrics include DSS, HaarPSI, MDSI, and VIF, which assess structural, wavelet-based, gradient/chromaticity, or information-fidelity relationships between images.Higher image quality corresponds to higher values for these full-reference metrics.
  • 1) Image-based metrics:: Fig. 2 illustrates corrupted and repaired images, BRISQUE variation across inference steps, the ASP-selection system model, and an experiment demo.

2) A General Modelling of Perceived Image Quality Metric:

The paper models perceived image quality as a function of diffusion inference steps and fits a four-parameter representation from experiments.

  • Excessive inference steps consume resources even though diffusion-based image quality may improve initially, motivating experiments across six perceived-quality metrics.The metrics are TV, BRISQUE, DSS, HaarPSI, MDSI, and VIF.
  • The proposed model uses Ax, Ay, Bx, and By to represent the onset and stabilization of quality improvement and the corresponding lower and highest quality values.The four points can be identified experimentally for positively or inversely proportional metrics and different AaaS types.
  • Experiments with simple fitting show that perceived quality varies approximately proportionally with inference steps despite diffusion-process uncertainty, enabling the model’s use in wireless edge AaaS analysis.

IV. DEEP REINFORCEMENT LEARNING-AIDED DYNAMIC ASP SELECTION

The paper formulates dynamic ASP selection as a utility-maximization problem under unknown provider quality and resource constraints, using a DRL solution in a multi-user edge setting.

  • IV. DEEP REINFORCEMENT LEARNING-AIDED DYNAMIC ASP SELECTION: Fig. 3 relates inference-step count to TV, BRISQUE, DSS, HaarPSI, MDSI, and VIF, supporting analysis of resource-dependent perceived quality.
  • A. AaaS System Model: Different ASP models trained on CelebA-HQ and Places2 produce different results for the same corrupted-image task, illustrating dataset-dependent service quality.
  • A. AaaS System Model: The study considers 20 ASPs and 1000 edge users whose tasks request random diffusion-timestep resources under Poisson arrivals over 288 hours.ASP capacities range from 600 to 1500 timesteps, task demands from 100 to 250 timesteps, and the total task count is 1000.
  • A. AaaS System Model: Dynamic ASP selection must maximize overall system utility while reducing overload and task interruption when provider quality is unknown to users.Choosing only the highest-quality ASP can overload it, while repeated probing adds network and computational load.

B. Deep Reinforcement Learning-based Solution

The paper formulates dynamic ASP selection as a utility-maximization problem and solves it with soft actor–critic DRL using task, resource, quality, and congestion information. Across benchmark comparisons, the DRL policy learns to avoid crashes and select higher-quality ASPs, achieving rewards comparable to the upper bound.

  • Method: Soft actor–critic DRL alternates critic-based policy evaluation with actor-based improvement while trading expected return against entropy.The method targets utility maximization while satisfying users’ requirements.
  • State and action design: The state represents the arriving task’s resource demand and estimated completion time together with every ASP’s total and currently available resources.
  • State and action design: The actor selects one ASP from a 20-dimensional softmax probability distribution for each arriving task.
  • Reward design: The reward combines perceived repaired-image quality with congestion penalties for overloads, crashes, and interrupted ongoing tasks.Larger penalties encourage the policy to avoid crashes.
  • Results: DRL’s reward rises past round-robin and overloading-avoidance during learning, then reaches an episodic reward comparable to the upper-bound policy.The benchmarks include random, round-robin, overloading-avoidance, and an upper-bound policy with posterior quality knowledge.
  • Results: Zero task crashes and higher cumulative episodic reward distinguish the DRL-enabled ASP selection policy across the five-policy comparison.DRL also increases average reward for finished tasks by combining crash avoidance with learned ASP quality selection.

V. FUTURE DIRECTION

Future AaaS deployments require stronger security for wireless transmission, content provenance, and training-data privacy. Suggested directions include improved physical-layer security, blockchain-based distribution and verification, and secure federated learning.

  • A. Secure AIGC-as-a-Service: Future AaaS research should protect transmitted AIGC data with improved physical-layer security techniques.
  • A. Secure AIGC-as-a-Service: Blockchain could support decentralized content distribution and verification of AIGC authenticity and provenance.
  • A. Secure AIGC-as-a-Service: Secure federated learning is proposed to help guarantee privacy for biometric training data such as face images.

B. IoT-based and Wireless Sensing-aided Passive AaaS

The paper proposes wireless sensing-aided passive AaaS, highlights personalized resource allocation as an open direction, and concludes with a framework spanning AaaS concepts, quality modeling, and ASP selection.

  • B. IoT-based and Wireless Sensing-aided Passive AaaS: Wireless sensing can support passive AaaS by feeding environmental or behavioral data into AIGC models for content such as personalized workout plans.Mobility of network devices remains relevant because it affects throughput on connected AaaS links.
  • C. Personalized Resource Allocation in AaaS: Personalized AaaS requires adapting resource allocation and model assignment to users’ roles and preferences, beyond generating the same output for identical prompts.For example, a horse trainer and a monkey researcher may require different image emphasis or model selection for the same text-to-image task.
  • VI. CONCLUSION: The article proposes AaaS, a resource–perceived-quality model, and a DRL-enabled algorithm for nearly optimal ASP selection.It also reviews AIGC technologies, wireless-network applications, deployment challenges, and image-based performance metrics.
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