Multi video stream collaborative adaptive offloading scheme based on equilibrium game theory

Massive video stream transmission and analysis require a large amount of bandwidth and computing resources, which poses a serious challenge to the current video stream offloading scheme based on mobile edge computing (MEC). A self-adaptive offloading scheme based on a balanced game multi video stream collaborative optimization framework is proposed for this purpose. Firstly, under the constraint of long-term MEC energy budget, the processing cost of video tasks is minimized by jointly optimizing data stream selection decisions, server offloading decisions, bandwidth resource allocation, and computing resource allocation. Secondly, an adaptive task offloading algorithm based on game theory and Nash equilibrium is designed to achieve optimal node selection through offloading algorithm and weight allocation, minimizing the processing cost of the entire multi video stream system and balancing video stream computation latency and energy consumption; Finally, the simulation results show that the proposed scheme satisfies long-term MEC energy constraints while significantly outperforming existing benchmark research schemes in terms of cost performance.