Optimal and Robust QoS-Aware Predictive Adaptive Video Streaming for Future Wireless Networks
Bibliographic record
Abstract
The exploitation of mobility traces and rate predictions has enabled predictive delivery of video content that can achieve optimal resource utilization and long-term Quality of Service (QoS) satisfaction. The network recognizes users moving towards poor radio conditions in order to prioritize them over other users with better future conditions. In this paper, we propose a QoS-aware predictive Dynamic Adaptive Streaming over HTTP (DASH) scheme that leverages future information to select both the resource sharing and video qualities over a time horizon. The scheme minimizes the number of quality switches while achieving a minimal average quality level with no video stops. We firstly define the maximum prediction gains under idealistic conditions by a scheme referred to as Optimal QoS-Aware Predictive-DASH (OQP-DASH). Then, a robust stochastic based formulation is introduced to handle the practical uncertainty in predicted information, where the scheme is denoted by Robust QoS-Aware Predictive-DASH (RQP-DASH). A chance constraint programming model based on Scenario Approximation (SA) is adopted to cap the risk of service degradation while using the Probability Mass Function (PMF) of predicted rates. Under idealistic conditions, OQP-DASH outperforms the non-predictive opportunistic counterpart and results in fewer quality switches. Applying estimation errors, RQP-DASH avoids QoS degradation without compromising the prediction gains which supports the application of predictive DASH in future network.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".