Cache-Enabled XR Systems: Delay-Aware Resource Allocation for Immersive Experience
Bibliographic record
Abstract
Extended Reality (XR) applications offer immersive experiences across industrial, healthcare, educational, and entertainment sectors, but they demand ultra-low latency and high data rates that challenge current cellular infrastructure. This paper proposes a latency-aware mobile XR system comprising multi-antenna base stations (BSs) and edge servers, each equipped with limited fronthaul capacity and local caching. To minimize end-to-end latency, we develop a unified optimization framework that jointly addresses field of view (FOV) caching and rendering, BS selection, beamforming vector design, and edge server placement. The framework captures the inter-dependencies between user-specific FOVs, rendering decisions, and resource constraints such as computation capacity and power, ultimately enhancing the quality of personal experience (QoPE). We formulate the problem as a mixed-integer non-convex program and solve it using ℓ0-norm relaxation, successive convex approximation, and fractional programming. Reformulating it as a multiple choice multiple dimensional knapsack problem (MMKP), we apply Lagrangian dual decomposition to derive efficient solutions. Simulation results demonstrate that our approach significantly outperforms baseline algorithms. Notably, a 91% reduction in average delay is achieved when varying BS cache size, and a 94% improvement is observed over the greedy-edge method when adjusting edge server cache size. These results highlight the potential of the proposed method for scalable, delay-aware XR systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.010 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".