Digital Twin-Assisted Adaptive Preloading for Short Video Streaming
Bibliographic record
Abstract
We propose a digital twin-assisted adaptive preloading scheme to reduce bandwidth waste as well as enhance user quality of experience (QoE) for short video streaming. Though preloading video content can reduce rebuffering and improve user QoE, non-sequential playback of short videos induced by user swipe can result in substantial bandwidth wastage in mobile networks. To tackle this problem, we first model the short video streaming system and carry out preloading threshold analysis. We then construct a digital twin-assisted adaptive preloading framework for short video streaming. By collecting and analyzing the user's historical throughput and tracking swipe timing information, a throughput prediction model and a probabilistic model can be constructed to accurately predict future throughput and user swipe behavior, respectively. Utilizing the predicted information and real-time running status data from a short video application, we design a preloading strategy to enhance bandwidth efficiency while achieving high user QoE. Simulation results demonstrate the effectiveness of our proposed scheme compared with the state-of-the-art schemes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".