Resource Allocation and Pricing for Blockchain-enabled Metaverse: A Stackelberg Game Approach

TL;DR

This paper models resource allocation and pricing in blockchain-enabled metaverse services using a Stackelberg game, proposing an algorithm that enhances provider profit and convergence speed through extensive simulations.

Contribution

It introduces a novel Stackelberg game framework for resource pricing in blockchain-based metaverse, along with an efficient algorithm to optimize profit and resource allocation.

Findings

The proposed GSRAP algorithm outperforms baseline schemes in profit and speed.

Mathematically proved the existence of Stackelberg equilibrium.

Simulation results confirm the effectiveness of the proposed approach.

Abstract

As the next-generation Internet paradigm, the metaverse can provide users with immersive physical-virtual experiences without spatial limitations. However, there are various concerns to be overcome, such as resource allocation, resource pricing, and transaction security issues. To address the above challenges, we integrate blockchain technology into the metaverse to manage and automate complex interactions effectively and securely utilizing the advantages of blockchain. With the objective of promoting the Quality of Experience (QoE), Metaverse Service Users (MSUs) purchase rendering and bandwidth resources from the Metaverse Service Provider (MSP) to access low-latency and high-quality immersive services. The MSP maximizes the profit by controlling the unit prices of resources. In this paper, we model the interaction between the MSP and MSUs as a Stackelberg game, in which the MSP acts as the leader and MSUs are followers. The existence of Stackelberg equilibrium is analyzed and proved mathematically. Besides, we propose an efficient greedy-and-search-based resource allocation and pricing algorithm (GSRAP) to solve the Stackelberg equilibrium (SE) point. Finally, we conduct extensive simulations to verify the effectiveness and efficiency of our designs. The experiment results show that our algorithm outperforms the baseline scheme in terms of improving the MSP's profit and convergence speed.