Trusted Collaboration for MEC-Enabled VR Video Streaming: A Multi-Agent Reinforcement Learning Approach
Bibliographic record
Abstract
Collaboration among mobile edge computing (MEC) has been envisioned as a promising paradigm to meet the requirements of wireless virtual reality (VR) applications. However, trust risks create tremendous challenges in MEC collaboration due to the distributed, complex, and unreliable nature of resource providers. In this paper, we present a trusted collaboration framework for VR video streaming to manage the video buffer in VR devices (VDs) under a more realistic distributed environment. In the framework, the rendering tasks can be processed collaboratively among edge servers (ESs) by exploring their behaviors (e.g., selfish behavior, malicious behavior, and cooperative behavior). Considering the collaborator may not be fully trustworthy, we present a novel trust evaluation method by combining direct and indirect values, aiming to ensure reliable collaborator selection. Then, we formulate an optimization problem to maintain an effective buffer state in VR devices (VDs) through jointly optimizing collaborator selection, spectrum allocation, and rendering resource allocation. Due to the fluctuating wireless fading channel and the dynamic video rate, the optimization problem is intractable by adopting traditional methods. Then, we adopt the multi-agent deep deterministic policy gradient (MADDPG) to tackle this dynamic and distributed problem. Simulation results indicate that the proposed approach can achieve a good performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".