Toward Attention-Aware Interactive 360° Video Streaming on Smartphones
Bibliographic record
Abstract
Browsing 360-degree videos on smartphones is becoming increasingly popular, which is challenging due to heavy user interactions, limited hardware capabilities, and constrained batteries. To improve resource efficiency and user experience, we propose a framework for attention-aware interactive 360-degree video streaming for smartphone users. We first explore the built-in capabilities of off-the-shelf smartphones to capture user attention during video browsing. Using the information of user attention to predict viewport and analyze user-perceptive quality, we then investigate the approaches for improving video encoding, transmission, and rendering in 360-degree video streaming. Further, we discuss the opportunities for enhancing user interactive experience with error tolerance, protocol design, and on-device computation. Finally, our prototype implementation is presented as a case study to validate our design principles. Our framework integrates and coordinates advanced techniques for different components of 360-degree video streaming systems to achieve better user experience, which can also serve as a foundation for leveraging modern attention-based multi-modal learning architectures.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".