Edge-Based Video Stream Generation for Multi-Party Mobile Augmented Reality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the popularity of mobile devices and the continuous advancement of mobile network technology, running online augmented reality (AR) on lightweight mobile devices is much more desirable than on heavy and expensive head-mounted devices that are difficult to satisfy users. Mobile edge computing can assist in supporting AR applications running on mobile devices, which copes with compute-intensive and delay-sensitive requirements. However, subject to the limited and heterogeneous edge resources, offloading tasks to edge devices is not easy, especially if the application requires multi-party interaction. It is challenging to develop a credible task placement scheme that satisfies user experience with flexible use of edge resources. This article focus on the task offloading placement problem for AR overlay rendering in multi-party mobile augmented reality system. We first present our observations about performance bottlenecks of edge devices and explain the necessity of splitting the AR overlay rendering pipeline. We then formulate a joint optimization problem of task placement decisions, aiming to maximize the user experience of quality and minimize the service cost. We develop a novel decision approach based on deep reinforcement learning (DRL) to address this complex problem. Finally, we verify the effectiveness and superiority of the proposed method through extensive evaluation experiments.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it