  Metaverse applications such as virtual reality (VR) content streaming,
require optimal resource allocation strategies for mobile edge computing (MEC)
to ensure a high-quality user experience. In contrast to online reinforcement
learning (RL) algorithms, which can incur substantial communication overheads
and longer delays, the majority of existing works employ offline-trained RL
algorithms for resource allocation decisions in MEC systems. However, they
neglect the impact of desynchronization between the physical and digital worlds
on the effectiveness of the allocation strategy. In this paper, we tackle this
desynchronization using a continual RL framework that facilitates the resource
allocation dynamically for MEC-enabled VR content streaming. We first design a
digital twin-empowered edge computing (DTEC) system and formulate a quality of
experience (QoE) maximization problem based on attention-based resolution
perception. This problem optimizes the allocation of computing and bandwidth
resources while adapting the attention-based resolution of the VR content. The
continual RL framework in DTEC enables adaptive online execution in a
time-varying environment. The reward function is defined based on the QoE and
horizon-fairness QoE (hfQoE) constraints. Furthermore, we propose freshness
prioritized experience replay - continual deep deterministic policy gradient
(FPER-CDDPG) to enhance the performance of continual learning in the presence
of time-varying DT updates. We test FPER-CDDPG using extensive experiments and
evaluation. FPER-CDDPG outperforms the benchmarks in terms of average latency,
QoE, and successful delivery rate as well as meeting the hfQoE requirements and
performance over long-term execution while ensuring system scalability with the
increasing number of users.
