Virtual reality (VR) streaming demands both high data rates and low latency, requiring advanced transmission strategies to enhance system performance in wireless networks. This paper proposes a hierarchical rate-splitting multiple access-based cell-free (HRS-CF) VR transmission strategy, which integrates field of view (FoV)-based user grouping, scalable video coding (SVC)-based message design, and message-centric base station selection. Moreover, to characterize the statistical data rate of VR services under a given delay constraint, we investigate the effective capacity (EC) of the VR video streaming under HRS-CF strategy. Furthermore, we jointly optimize the precoding matrix, rate splitting coefficients, and BS selection using the proximal policy optimization (PPO) algorithm, where a power threshold-based BS selection is introduced to reduce computational complexity. Simulation results show that the proposed HRS-CF strategy outperforms the conventional rate splitting and CF transmission schemes, achieving at least a 16% performance improvement.

Prompt learning is an effective way to adapt pre-trained models to downstream tasks by training a small number of additional learnable prompts. Recent studies address several early challenges by combining generalized knowledge from frozen pre-trained VL models with task-specific knowledge from training data as guidance for prompt learning. However, existing methods still struggle with the generalization-adaptation (GA) trade-off dilemma: excessive reliance on generalized knowledge hinders adaptation to downstream tasks, while overemphasis on task-specific knowledge undermines the inherent generalization capabilities of pre-trained models. To address this issue, we propose a novel prompt learning method called Prompt Learning with Knowledge Regularization (PLKR). PLKR effectively mitigates the GA trade-off dilemma by offering greater flexibility in adapting to task-specific knowledge while minimizing the disruption of pre-trained knowledge. Specifically, we propose category-invariant and topology-invariant knowledge regularization to preserve generalized knowledge: the former enhances category-level discriminative capabilities while allowing flexible task-specific learning, and the latter maintains global topological stability during adaptation to new tasks. Through the proposed regularization, PLKR improves the performance on both base and new tasks. We evaluate the effectiveness of our approach on four representative tasks over 11 datasets. Experimental results show our method outperforms existing SOTA methods by a large margin.

Inter-cell interference poses a significant challenge to the performance and reliability of cellular networks due to the complex spatial and temporal relationships between network nodes. Addressing this issue requires accurate prediction and assessment of interference. This paper presents a novel solution leveraging the strengths of a weighted graph convolutional network (WGCN) combined with graph coloring techniques. Specifically, we propose a WGCN-based interference estimation model to accurately derive the real-time inter-cell interference. Then, a graph multi-coloring problem is considered for the interference coordination. To address the color collision between cells and the color (i.e. spectrum resources) requirement of individual cells in the graph coloring problem, we propose a WGCN-assisted graph multi-coloring (WGCN-GMC) algorithm to allocate spectrum resources rationally. Simulation results demonstrate that our approach significantly enhances interference coordination, and achieves an impressive average improvement of 58.2 % compared to the traditional GMC algorithm leading to improved overall network performance.

Given the popularity of flawless telepresence and the resultants explosive growth of wireless video applications, besides handling the traffic surge, satisfying the demanding user requirements for video qualities has become another important goal of network operators. Inspired by this, cooperative edge caching intrinsically amalgamated with scalable video coding is investigated. Explicitly, the concept of a Pareto-optimal semi-distributed multiagent multipolicy deep reinforcement learning (SD-MAMP-DRL) algorithm is conceived for managing the cooperation of heterogeneous network nodes. To elaborate, a multipolicy reinforcement learning algorithm is proposed for finding the Pareto-optimal policies during the training phase, which balances the teletraffic versus the user experience tradeoff. Then the optimal policy/solution can be activated during the execution phase by appropriately selecting the associated weighting coefficient according to the dynamically fluctuating network traffic load. Our experimental results show that the proposed SD-MAMP- acrshort DRL algorithm: 1) achieves better performance than the benchmark algorithms and 2) obtains a near-complete Pareto front in various scenarios and selects the optimal solution by adaptively adjusting the above-mentioned pair of objectives.

Mobile edge computing (MEC) and field-of-view (FoV) prediction are two key techniques to enable the wireless virtual reality (VR) service. On this basis, we investigate a practical issue, that is, how to efficiently achieve the user's pre-set quality-of-experience (QoE) requirement on both video quality and delay tolerance. A constrained reward-steering algorithm based on reinforcement learning is proposed in this work to solve this multi-objective optimization problem, which finds the optimal policy approaching the user's targeted QoE. Meanwhile, both an instantaneous service delay constraint and a long-term energy constraint are satisfied by the Lagrangian-based method. Simulation results demonstrate that the proposed algorithm outperforms conventional reinforcement learning relying on weights, i.e. achieving an average reward vector much closer to the user's targeted QoE, and meeting both constraints.