Viewing Distance-Aware Volumetric Video Caching and Rendering for XR Services
Bibliographic record
Abstract
In this paper, we propose a novel volumetric video caching and rendering approach tailored for interactive extended reality (XR) services, aiming to enhance users’ quality of experience (QoE). Specifically, an XR user’s QoE is jointly determined by the actual quality of the video delivered to the user’s XR device and the distance between the user’s viewpoint and the location of virtual objects within the videos. Thus, we adopt a customized QoE model and develop an adaptive caching and rendering approach for XR where the users’ viewing distances vary over time. In our proposed approach, point clouds representing virtual objects with various densities can be dynamically cached at the edge server, and then the most appropriate cached point cloud will be selected for rendering to each user. A long-term optimization problem is formulated to maximize the accumulated QoE of XR users over time. Solving the optimization problem is very challenging due to the unavailability of future information and the NP-hardness. Therefore, we first use a regularization technique to decouple the optimization problem into a series of one-shot optimization subproblems, whose relaxed optimal solutions can be efficiently obtained by using convex tools. Then, we design a dependent rounding algorithm to recover relaxed solutions to integral solutions without violating network resource constraints. Simulation results demonstrate that our proposed approach outperforms two benchmark algorithms in terms of the accumulated QoE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".