Energy-Efficient Multi-User Adaptive 360$^{\circ }$ Video Streaming: A Two-Step Approach With Device Video Super-Resolution
Bibliographic record
Abstract
In this paper, we propose a device energy-efficient two-step adaptive scheme for tile-based 360<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> video streaming to support enhanced multi-user viewing quality-of-experience (QoE) in a time-slotted system. Specifically, each video chunk is first prefetched based on the predictive field-of-view (FoV), and the FoV quality of the chunk is then enhanced at a closer-to-playback time instant based on the updated FoV prediction with improved accuracy. Both transmission-driven and device video super-resolution (VSR)-driven methods are adaptively selected to enable efficient video chunk enhancement. At each time slot, the incremental QoE gain of each user is characterized via a time-difference approach, based on which the best candidate chunk is determined. Then, a single-slot problem is formulated for maximizing the total incremental QoE gain while minimizing the total device energy consumption. A particle swarm optimization (PSO)-based iterative solution is proposed to obtain optimal bandwidth allocation, bitrate level selection, and enhancement method selection for multiple users. Extensive simulation results demonstrate that our proposed solution outperforms benchmark schemes in terms of average viewing QoE, average device energy consumption, and average utility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".