Stride: Distributed Video Transcoding in Spark
Why this work is in the frame
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Bibliographic record
Abstract
On one hand, since the introduction of UHD (ultra-high definition) videos, e.g., 4K and 8K videos, it is becoming more resource and time intensive to transcode videos. On the other hand, the increasing demand for video streaming implies more videos need to be transcoded. These two facts motivate the need for techniques to speedup coding and transcoding time. In this paper, we propose Stride, the first distributed video transcoding system that leverages the Apache Spark big data platform. The design of Stride is transcoder agnostic, meaning it can adopt any transcoder implementation (e.g., FFMPEG) without any modification. We provide an experimental characterization of the impact of video transcoding and Spark configuration parameters to identify the optimal settings. We also compare Stride with competing approaches. Our results show that Stride achieves 3.27 times speedup when the computing power (i.e., the number of vCPUs in a cloud) is increased by a factor of 4, which is significantly higher than the other alternatives we explore. In particular, Spark's dynamic task scheduler allows Stride to reduce transcoding time by 19.86% compared to an implementation without Spark. Our benchmark study suggests that Stride can support transcoding from 4K to 1080p (full HD) at a rate matching the video bitrate using approximately only 24 virtual cores.
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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 | 0.001 |
| 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