arXiv:2402.09729v1 Announce Type: new Abstract: This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system. We first introduce a quality of experience (QoE) metric to measure user experience, which considers the MEC system’s latency, user attention levels, and preferred resolutions. Then, a QoE maximization problem is formulated for resource allocation to ensure the highest possible user experience,which is cast as a reinforcement learning problem, aiming to learn a generalized policy applicable across diverse user environments for all MEC servers. To learn the generalized policy, we propose a framework that employs federated learning (FL) and prompt-based sequence modeling to pre-train a common decision model across MEC servers, which is named FedPromptDT. Using FL solves the problem of insufficient local MEC data while protecting user privacy during offline training. The design of prompts integrating user-environment cues and user-preferred allocation improves the model’s adaptability to various user environments during online execution.
The article “Resource Allocation for Customized Virtual Reality Services in Mobile Edge Computing Systems” explores the challenges and solutions in providing personalized virtual reality (VR) experiences to users in a mobile edge computing (MEC) system. The authors introduce a quality of experience (QoE) metric that takes into account factors such as latency, user attention levels, and preferred resolutions. They then formulate a QoE maximization problem as a reinforcement learning task, aiming to learn a generalized policy applicable across diverse user environments for all MEC servers. To achieve this, they propose a framework called FedPromptDT, which combines federated learning and prompt-based sequence modeling to pre-train a common decision model while protecting user privacy. The use of prompts that integrate user-environment cues and preferred allocation enhances the model’s adaptability to different user scenarios during online execution. Overall, this research tackles the resource allocation challenge in delivering customized VR services, leveraging advanced techniques such as reinforcement learning and federated learning.
Resource Allocation for Customized VR Services in Mobile Edge Computing
Virtual reality (VR) has gained significant popularity in recent years, providing immersive experiences to users across various domains. As the demand for VR services grows, it becomes crucial to ensure high-quality user experiences. This paper delves into the concept of resource allocation in a mobile edge computing (MEC) system to cater to the diverse needs of heterogeneous users.
Introducing the Quality of Experience (QoE) Metric
In order to measure user experience accurately, we propose the use of a comprehensive quality of experience (QoE) metric. This metric takes into account various factors such as MEC system latency, user attention levels, and preferred resolutions. By considering these aspects, we can gauge the overall satisfaction of users and identify areas for improvement.
A Reinforcement Learning Approach
To optimize resource allocation for maximizing QoE, we formulate the problem as a reinforcement learning (RL) problem. RL allows us to learn a generalized policy that can be applied across different user environments for all MEC servers. By leveraging RL techniques, we can continuously adapt and improve the resource allocation strategy based on real-time feedback.
FedPromptDT: A Framework for Learning Generalized Policies
In order to learn the generalized policy effectively, we propose a novel framework called FedPromptDT. This framework combines federated learning (FL) with prompt-based sequence modeling to pre-train a common decision model across MEC servers. FL addresses the challenge of limited local MEC data while also ensuring user privacy during offline training.
Prompts for Enhanced Adaptability
One key aspect of FedPromptDT is the design of prompts that integrate user-environment cues and user-preferred allocation. By incorporating these cues into the decision-making process, the model becomes more adaptable to various user environments during online execution. This ensures that the resource allocation strategy is personalized to each user’s individual needs and preferences.
Innovative Solutions for Customized VR Services
By leveraging the proposed framework, resource allocation for customized VR services in MEC systems can be significantly improved. The use of a comprehensive QoE metric allows for a holistic evaluation of user experiences, leading to targeted optimizations. The reinforcement learning approach ensures continuous improvement based on real-time feedback, while the FedPromptDT framework addresses challenges related to data availability and user privacy.
This research opens up new possibilities for the future of VR services, enabling seamless and personalized experiences for users across different environments. As VR technology continues to advance, it is essential to focus on optimizing resource allocation to meet the diverse needs and preferences of users.
“The combination of FL and prompt-based sequence modeling in the FedPromptDT framework offers a promising solution for learning generalized policies in resource allocation.”
With further advancements in RL techniques and the integration of user feedback, we can anticipate even more tailored and immersive VR experiences in the near future. This research lays the foundation for ongoing developments in resource allocation strategies and paves the way for a more efficient and user-centric VR ecosystem.
The paper addresses the challenge of resource allocation in a mobile edge computing (MEC) system to provide customized virtual reality (VR) services to heterogeneous users. The goal is to maximize the quality of experience (QoE) for users, taking into account factors such as latency, user attention levels, and preferred resolutions.
One of the key contributions of the paper is the introduction of a QoE metric that comprehensively measures user experience. This metric goes beyond traditional measures such as latency and incorporates user attention levels and preferred resolutions. This holistic approach is crucial in ensuring that the allocated resources truly meet the users’ needs and preferences.
To solve the resource allocation problem, the authors formulate it as a reinforcement learning problem. By casting it as a reinforcement learning problem, they aim to learn a generalized policy that can be applied across diverse user environments for all MEC servers. This is an important aspect as it allows for scalability and adaptability in real-world scenarios where the user environments may vary significantly.
To learn the generalized policy, the authors propose a framework called FedPromptDT, which combines federated learning (FL) and prompt-based sequence modeling. FL is employed to address the challenge of insufficient local MEC data, as it allows for collaborative learning across multiple MEC servers without compromising user privacy during offline training. This is particularly important in scenarios where user data privacy is a concern.
The prompt-based sequence modeling approach enhances the model’s adaptability to various user environments during online execution. By incorporating user-environment cues and user-preferred allocation in the prompts, the model can dynamically adjust its resource allocation decisions based on the specific context and user preferences at runtime. This increases the model’s effectiveness in real-world scenarios where user needs and preferences may change over time.
Overall, this paper presents a comprehensive approach to resource allocation for customized VR services in MEC systems. By considering a holistic QoE metric, formulating the problem as a reinforcement learning task, and leveraging FL and prompt-based sequence modeling, the authors address key challenges in providing optimal user experiences in diverse user environments while protecting user privacy. Moving forward, it would be interesting to see how this approach performs in real-world deployments and how it can be further enhanced to handle even more complex scenarios and user requirements.
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