CONTRAST: Container-based Transcoding for Interactive Video Streaming
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
Interactive video streaming applications are becoming increasingly popular. To maintain the Quality of Experience (QoE) of an end user, interactive streaming platforms need to transcode a video stream, i.e., adapt the quality of the video content, to match the network conditions between the platform and the user as well as the device capabilities of the end user. Modern video codecs such as High Efficiency Video Coding (HEVC) require significant computational resources for transcoding operations. Consequently, there is a need for systems that can perform transcoding quickly at runtime to sustain the real-time performance required for interactive streaming while at the same time using just the right amount of computational resources for the transcoding operations. This paper addresses this need by designing and implementing CONTRAST, a Container- based Distributed Transcoding Framework for Interactive Video Streaming. For any given stream and transcoding resolution, CONTRAST exploits a profiling technique to automatically determine the degree of parallelism, Le., the number of processing cores, demanded by the transcoding process to sustain the stream’s frame rate. It then launches Docker containers configured with the required number of cores to perform the transcoding. Experiments using a set of realistic video streams show that CONTRAST is able to sustain the frame rate requirements for interactive streams in a more resource efficient manner compared to baseline techniques that do not consider the degree of parallelism. To the best of our knowledge, our paper is the first to establish best practices for implementing transcoding platforms for interactive streaming videos encoded using a modem video codec.
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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 | 0.000 |
| Open science | 0.001 | 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