Advancements in extended reality (XR) are driving the development of the metaverse, which demands efficient real-time transformation of 2D scenes into 3D objects, a computation-intensive process that necessitates task offloading because of complex perception, visual, and audio processing. This challenge is further compounded by asymmetric uplink (UL) and downlink (DL) data characteristics, where 2D data are transmitted in the UL and 3D content is rendered in the DL. To address this issue, we propose a digital twin (DT)-based in-network computing (INC)-assisted multi-access edge computing (MEC) framework that enables real-time synchronization and collaborative computing via URLLC. In this framework, a network operator manages wireless and computational resources for XR user devices (XUDs), while XUDs autonomously offload tasks to maximize their utilities. We model the interactions between XUDs and the operator as a Stackelberg Markov game, where the optimal offloading strategy constitutes an exact potential game with a Nash Equilibrium (NE), and the operator's problem is formulated as an asynchronous Markov decision process (MDP). We further propose a decentralized solution in which XUDs determine offloading decisions based on the operator's joint UL-DL optimization of offloading mode (INC-E or MEC only) and DL power allocation. A Nash-asynchronous hybrid multi-agent reinforcement learning (AMRL) algorithm is developed to predict the UL user-associated and DL transmission power, thereby achieving NE. Simulation results demonstrate that the proposed approach considerably improves system utility, uplink rate, and energy efficiency by reducing latency and optimizing resource utilization in metaverse environments.

翻译：扩展现实（XR）技术的进步推动了元宇宙的发展，其要求将二维场景高效实时地转化为三维对象。由于涉及复杂的感知、视觉和音频处理，这一计算密集型过程需要借助任务卸载。但上行（UL）与下行（DL）数据的不对称特性——上行传输二维数据，下行渲染三维内容——进一步加剧了实现该过程的挑战。为此，我们提出一种基于数字孪生（DT）的网络内计算（INC）辅助多接入边缘计算（MEC）框架，通过URLLC实现实时同步与协同计算。在该框架中，网络运营商管理XR用户设备（XUD）的无线与计算资源，而XUD则自主卸载任务以最大化其效用。我们将XUD与运营商间的交互建模为Stackelberg马尔可夫博弈：其中最优卸载策略构成一个具有纳什均衡（NE）的精确势博弈，运营商的优化问题则被建模为异步马尔可夫决策过程（MDP）。我们进一步提出一种去中心化解决方案：XUD根据运营商对卸载模式（仅INC-E或仅MEC）与下行功率分配的联合上下行优化来制定卸载决策。为预测上行用户关联与下行传输功率，我们开发了一种纳什异步混合多智能体强化学习（AMRL）算法，从而实现纳什均衡。仿真结果表明，所提方法通过降低延迟并优化资源利用率，在元宇宙环境中显著提升了系统效用、上行速率与能量效率。