A Multi-CDN Playground for Dash.js: Enabling Integration of CDN Switching Strategies
Bibliographic record
Abstract
The recent introduction of the ETSI TS 103 998 content steering standard marks a significant milestone in the evolution of media delivery for content providers. This standard simplifies the process of utilizing multiple Content Delivery Networks (CDNs), a key requirement for managing large-scale client bases. It offers clear guidelines for real-time CDN state switching, which enhances the user experience by dynamically selecting the most suitable CDN based on changing network conditions. In contrast, current industry solutions often resemble load balancing techniques, relying on heuristic approaches that are manually crafted and unable to adapt effectively to varying network environments. These static solutions frequently fail to optimize the user experience in real-time, especially when faced with diverse and unpredictable network conditions. In this paper, we utilize our previous work StreamWise---a DRL-based multi-CDN solution that outperforms existing state-of-the-art methods, offering superior adaptability and efficiency. We integrate this solution into the Dash.js reference player. Through a comprehensive demonstration, we illustrate how StreamWise, or any other multi-CDN solution, can be seamlessly incorporated into Dash.js, providing a benchmark for future developments in multi-CDN switching solutions. Experimental results in the wild demonstrate the performance of various multi-CDN solutions for Dash.js in Video on Demand (VoD) streaming mode. On average, StreamWise provides a ~78% improvement in VMAF compared to other solutions, while also ensuring smooth quality transitions., while also ensuring smooth quality transitions.
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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 | 0.000 |
| 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 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".