LiveStream Meta-DAMS: Multipath Scheduler Using Hybrid Meta Reinforcement Learning for Live Video Streaming
Bibliographic record
Abstract
Overcoming challenges in mobile environments, such as bandwidth constraints, user mobility, and network hand-offs, is crucial for video streaming applications. To address these challenges, we can use multiple network paths to mitigate bandwidth limitations and guarantee end-to-end delay, enhancing the overall quality of experience for the users. This paper presents LiveStream Meta Learning-based Delay Aware Multipath Scheduler (LSMeta-DAMS), a novel learning-based multipath scheduler explicitly designed for live streaming applications. LSMeta-DAMS employs a hybrid meta-reinforcement learning architecture, incorporating both online and offline phases to enhance speed and accuracy for training and decision making. Prioritizing packet scheduling based on frame types and considering the video coding features like group of pictures (GOP), scalable video coding (SVC), and Dynamic Adaptive Streaming over HTTP (MPEG-DASH), LSMeta-DAMS offers a tailored solution for multipath video streaming. Trace-driven emulations highlight its superior performance, demonstrating up to 32% improvement in learning, up to 25% reduction in download time, up to 15% enhancement in video quality assessment, and up to 35% reduction in stalling time compared to the state-of-the-art multipath schedulers. These findings underscore LSMeta-DAMS’s potential to substantially enhance video streaming experiences in highly dynamic network conditions.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".