A video encoding speed-up architecture for cloud gaming
Bibliographic record
Abstract
In cloud-based video gaming systems, game engines are hosted in the cloud, and rendered gaming scenes are streamed to players over the Internet. In such systems, the tasks of rendering graphics and video encoding impose huge computational complexity on cloud servers. Therefore, speeding up the encoding process to meet the stringent requirements of the game becomes a critical issue in cloud-based video gaming systems. In this paper, we analyze the feasibility of developing a mechanism to accelerate the power-intensive process of video encoding, by using available game objects information in game engines. Specifically, we utilize the game engine's information about the motion of the objects within the scene in order to bypass the time-consuming procedure of Motion Estimation (ME) in conventional video encoders like H.264/AVC. Based on our analysis, the game engine's information could be usable inside a video encoder if an interface is involved to re-shape object information and make them compatible for the video encoder. Our experiments show that our approach accelerates the motion estimation process by 14.32% on average for two specific games, when object's information is taken into account during the encoding phase.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".