QoE-fair Adaptive Streaming of Free-viewpoint Videos over LTE Networks
Bibliographic record
Abstract
Free-viewpoint video (FVV) applications enable viewers to interactively change their viewing point and watch a scene from different angles. Each FVV is composed of multiple streams representing the scene and its geometry from different vantage points. In addition, virtual views can be synthesized from captured views to provide a smoother and more immersive experience to users. Delivering FVV streaming services over cellular networks, while achieving quality-of-experience (QoE) fairness and minimizing fluctuations in perceived quality, is very challenging due to the large bandwidth requirements, the complex relationship between the bitrates of the transmitted streams and the quality of rendered virtual views, and the time-varying channel conditions. In this paper, we formulate FVV adaptive streaming as a multi-objective QoE-fairness problem and propose a heuristic algorithm to solve it efficiently. Our experiments show that the proposed algorithm achieves high QoE-fairness and provide users with high and stable qualities. It reduces quality variations by up to 32% on average while saving up to 18% of cellular bandwidth, compared to state-of-the-art approaches.
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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 | 0.000 | 0.001 |
| Open science | 0.002 | 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".