Scheduling and buffering mechanisms for remote rendering streaming in virtual walkthrough class of applications
Bibliographic record
Abstract
With the recent advances of wireless networking and mobile devices technologies, exploration of remote 3D virtual environments has received a great deal of attention nowadays where many applications can be envisioned, such as virtual guides malls, online gaming, just to name a few. The primary objective of this paper is to present the specification of a buffering mechanism and a scheduling solution for on-time image delivery for a class of applications that is based upon a remote virtual environment exploration. Similar to a video buffering, our approach involves buffering the images closest to the user's position within the virtual environment, thereby, providing the client device with the necessary imagery data for rendering future user's viewpoints, removing network jitter, and providing smooth virtual interactionIn this paper, in order to overcome the limitations of current mobile device graphics performance, we propose to use a remote rendering approach that streams the necessary images to a client device as the user explores the virtual environment, leaving only the display and a number of minor image-based rendering tasks to the local, less powerful mobile hardware. Using ns-2, we present and extensive set of simulation experiments to evaluate the performance of our scheme. Our results indicate clearly that our solution is able to guarantee on-time delivery for approximately 40 sessions with a 10 Mbps link
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".