Towards bridging online game playing and live broadcasting
Bibliographic record
Abstract
Recent years have witnessed the emergence and growth of Cloud Gaming, where players interact with the remote game instance and receive rendered game scenes in video stream. Meanwhile, broadcasting and viewing games through live streaming platforms, e.g., Twitch.tv, have become increasingly popular. The interaction and performance of the many modules involved in this new generation of gaming and streaming platforms have yet to be closely investigated. In this paper, we present an initial experiment-based performance study, in which we profile the architecture of realworld gaming and streaming platforms, namely the Open Broadcast Software (OBS) module and its connection to the Twitch server. Our investigation shows that the recording operation can greatly increase the CPU utilization and the power consumption can increase over 60% on the game streaming computer. The use of advanced hardware encoding found on modern GPUs can greatly alleviate these performance issues. Yet, through profiling, we show that hardware encoding can introduce remarkable delays to the whole pipeline. We track this to a complicated interplay between the CPUs power saving methods and the implementation of hardware encoders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".