EAAT: Environment-Aware Adaptive Transmission for Split-Screen Video Streaming
Bibliographic record
Abstract
With the tremendous growth of video contents and mobility demands, there is a need to develop more personalized video services. Split-screen services, such as picture in picture become more and more popular. Furthermore, the user's viewing environment affects the user's quality of experience (QoE). Therefore, video transmission of split-screen services face several major challenges, such as to quantify the impact of environmental factors on user's QoE; how to assess the user's QoE of the split-screen services; how to choose the bit-rate of each video stream to maximize user's QoE of the split-screen services. To address these challenges, in the paper, an environment-aware adaptive transmission (EAAT) scheme for split-screen video streaming is first presented. Then, we introduce a mathematical model for characterizing user's QoE to be affected by environmental factors in the proposed EAAT. In the model, the QoE of user's relationship with the viewing environment is proposed. Based on the model, a problem of maximizing user's QoE is formulated, and we develop a heuristic algorithm to solve the optimization problem. In addition, we conduct various trace-bandwidth experiments to rigorously evaluate the proposed EAAT scheme in different network environments, and show that EAAT can enrich the video quality while saving network resources.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".