A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges
Bibliographic record
Abstract
Driven by the quality of experience (QoE) requirement of video streaming applications in the smart city, smart education, immersive service, and connected vehicle scenarios, the existing network poses significant challenges, including ultra-high bandwidth, ultra-large storage, and ultra-low latency requirements, etc. Multi-access edge computing (MEC) is a potential technology, which can provide computation-intensive and caching-intensive services for video streaming applications to satisfy the requirement of QoE. Thus, focusing on video streaming schemes, a comprehensive summary of the state of the art applying MEC to video streaming is surveyed. Firstly, the related overview and background knowledge are reviewed. Secondly, resource allocation issues have been discussed. Thirdly, the enabling technologies for video streaming are summarized by taking account of caching, computing, and networking. Then, a taxonomy of MEC enabled video streaming applications is classified. Finally, challenges and future research directions are given.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".