Fuzzy Logic-based Adaptive Multimedia Streaming for Internet of Vehicles.

Multimedia streaming for the Internet of Vehicles has the potential to enhance road safety and transport efficiency for autonomous vehicles, and the in-car experience for passengers. MPEG-Dynamic Adaptive Streaming over HTTP (MPEG-DASH) framework has been widely deployed to optimize video streaming with respect to end-user Quality of Experience (QoE). However, existing heuristic-based, reinforcement learning-based, or fuzzy-based adaptive algorithms, which use complex control laws and decision-making processes, are not well-suited to handle the non-stationary nature of road traffic environments. Consequently, these solutions often struggle to deliver optimal performance across multiple QoE objectives and under diverse network conditions. In this paper, we introduce FLAME, a novel adaptive multimedia streaming solution based on advanced fuzzy logic. FLAME incorporates interactive membership functions and fuzzy rules in its two variants, FLAME7 and FLAME5, resulting in reduced model complexities and training overheads. FLAME is adaptable to diverse video client settings and QoE goals. Our trace-driven experimental results demonstrate that FLAME solutions offer an improved uninterrupted streaming experience for connected vehicles. On average, FLAME outperforms other state-of-the-art solutions such as PENSIEVE, BOLA, FESTIVE, BBA, and ELASTIC by achieving 11.7% higher QoE.