iStream: A Flexible Container-Based Testbed for Multimedia Streaming
Bibliographic record
Abstract
Multimedia streaming is growing at a phenomenal rate, especially with the emergence of augmented/virtual reality applications and the growing demand for YouTube-like services. This stimulates research in all aspects of streaming systems, including but not limited to real-time and resource-efficient transcoding, server efficiency, network optimization, and client adaptation. Research and development of any part of the system require repetitive and tedious tasks in setting up the rest of the system for validation and evaluation of new ideas. This not only complicates the research work but also challenges the reproducibility and sharing of the research results. Existing proposed testbeds all have a specific focus and do not address all components of multimedia streaming. In this paper, we propose iStream, a flexible testbed for multimedia streaming system that integrates various components relevant to such systems. The components are plug-&-playable and customizable to compose various streaming systems and setups. iStream provides multimedia system researchers and developers with a flexible playground to explore their innovative ideas. Along with the design, we present a diverse set of case studies demonstrating the wide-range use of iStream.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".