Client-server cooperative and fair DASH video streaming
Bibliographic record
Abstract
Adaptive video streaming over HTTP, such as the MPEG-DASH standard, is now widely used by video service provides to stream their videos to users. But DASH and similar methods are known to suffer from two practical challenges: on the one hand, clients use fixed heuristics that limit their ability to generalize across network conditions, making the clients unable to efficiently predict variations in new networking environments, in turn leading to more buffering. On the other hand, the absence of collaboration among DASH clients leads to unfair bandwidth allocation, and typically pushes the system to an unbalanced equilibrium point. In this paper, we propose a server-side rate adaptation method that significantly improves the fairness of network bandwidth allocation among concurrent DASH users. We formulate the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) model, and use Reinforcement Learning (RL) to train two neural networks to find an optimal solution to the fairness problem. Since our solution is implemented at the server side, it requires no modifications to the widely-installed DASH clients, making our solution very practical. We show that our proposed method outperforms the state-of-the-art schemes in terms of QoE-efficiency, QoE-fairness, and social welfare by as much as 16%, 21%, and 24% respectively.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".