TSBG: A Two-stage Stackelberg Game Algorithm for QoE-awareness Video Streaming Transmission

Dynamic Adaptive Streaming over HTTP (DASH) stands as a leading streaming technology embraced by major video platforms and smart TV manufacturers worldwide. Despite its widespread use, the inherent diversity in both the video content and the client devices poses challenges, hindering DASH from consistently delivering top-notch playback quality for all users. This oversight often leads to network congestion, compromising the playback quality for users. To tackle these issues, we propose a Two-stage Stackelberg Game (TSBG) algorithm for personalized video streaming transmission in Edge Computing (EC) environments. The TSBG algorithm aims to optimize the Quality of Experience (QoE) of users by tailoring video streaming services between EC servers and clients. Initially, we establish the system model and define the video stream transmission problem as a multi-objective optimization problem, balancing server downlink resource scheduling and client adaptive bit rate. Subsequently, we design the TSBG algorithm, where an edge server allocation mechanism is adopted in the first stage to maximize overall user QoE, while users adjust their video bit rates based on the edge server's distribution plan to enhance their individual QoE in the second stage. We prove the existence and uniqueness of the equilibrium solution of the two-stage Starkelberg game and design an optimal pricing algorithm to maximize the benefits of edge servers. Extensive simulation experiments validate the effectiveness of the TSBG algorithm, showcasing its superiority in achieving enhanced QoE, network efficiency, and fairness compared to alternative approaches.