Interaction-Aware Resource Reservation for Edge-Assisted Mobile Extended Reality
Bibliographic record
Abstract
In this paper, we propose a novel interaction-aware resource reservation scheme for edge-assisted mobile extended reality (XR). Our objective is to minimize reserved resources for XR services while satisfying user requirements on quality of experience (QoE), including visual quality, quality variation, and round-trip latency. First, we develop a novel interactive mobility model for XR users, which captures both user mobility features and their interaction behaviors with XR content. Specifically, user interaction duration and viewing distance preferences are modeled as key parameters that can affect spatiotemporal service demand for XR content rendering and delivery. Second, we estimate the model parameters for individual users and use a sample average approximation method to assess the impact of communication and computing resource reservations on user QoE. Finally, we adopt a coordinate descent-based search algorithm to obtain the resource reservation decision. Simulation results demonstrate that our proposed scheme can effectively reduce network resource consumption in comparison with benchmark schemes while ensuring user QoE satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".