Rate adaptation strategy for video streaming over multiple wireless access networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Video streaming is gaining popularity among mobile users. The latest mobile devices, such as smart phones and tablets are equipped with multiple wireless network interfaces. How to efficiently and cost-effectively utilize multiple links to improve the video streaming quality needs to be investigated. In order to maintain high video streaming quality while reduce the wireless service cost, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MDP). The reward function is designed to consider the quality of experience (QoE) requirements for video traffic, such as the interruption rate, average playback quality, playback smoothness and wireless service cost. Using dynamic programming, the MDP can be solved to obtain the optimal streaming policy. To evaluate the performance of the proposed multi-link rate adaptation (MLRA) algorithm, we implement a testbed using the Android mobile phone and the open-source X264 video codec. Experimental results demonstrate the feasibility and effectiveness of the proposed MLRA algorithm for mobile video streaming applications, which outperforms the existing state-of-the-art one.
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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 | 0.000 | 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 it