Transfer Learning for Online Prediction of Virtual Reality Cloud Gaming Traffic
Bibliographic record
Abstract
Cloud-based Virtual Reality (VR) gaming is gaining popularity to provide immersive experiences without requiring bulky hardware. However, managing network resources for these games is crucial to prevent subpar user experience and unnecessary costs. Predicting VR traffic patterns, such as video frame sizes can enable proactive network resource allocation and lead to improved quality of service (QoS). To this end, in this paper we first evaluate the efficacy of various Machine Learning (ML) models for predicting gaming traffic frame sizes using data collected from a real-world cloud-based VR game testbed. We then investigate the effectiveness of transfer learning (TL) in predicting frame size traffic patterns across different games and network conditions using an online learning method that we propose. The findings show that using the TL approach for online learning prediction can reduce overall traffic prediction error by up to 54%. Overall, this paper contributes to the understanding of cloud-based VR traffic patterns and can be of interest to developers, practitioners, and researchers interested in optimizing the performance of such systems.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".