QoE-aware joint scheduling of buffered video on demand and best effort flows
Bibliographic record
Abstract
Since video services are expected to constitute a major portion of the mobile downlink traffic, it is important to consider end users' perceptual quality of experience (QoE) for video traffic in the system design and performance evaluation of next generation mobile networks. We present a novel QoE-aware scheduling scheme for buffered video on demand (VoD). Therein, rebuffering is the critical attribute undermining users' QoE. Our scheduling scheme is based on `vacuum pressure scheduling'. We use playback buffer vacancy and apply the `backpressure' scheduling theory to schedule the VoD flows. The proposed scheme is further adjusted to enable joint scheduling of a mixture of VoD and best effort (BE) flows within the same band. A simple control knob is provided to operators to softly adjust the region in which BE flows contend on resources. Results demonstrate substantial user capacity gains compared to prior work without compromising the QoS of BE flows. Sensitivity analysis to feedback periodicity shows remarkable robustness and overhead savings compared to the baseline. Results even hold when requested videos are of heterogeneous qualities, i.e., encoding rates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".