Delivering real-time multimedia content for safety applications over vehicular networks presents significant challenges due to rapidly changing network topology, high node mobility, and fluctuating traffic demands, compounded by substantial data volumes and network resource limitations. This paper proposes the Multi-Path Transmission Protocol for Video Streaming (MPTP-VS) to address these issues by optimizing video transmission in road incident scenarios. Utilizing a Fog Computing architecture, the protocol aims to enhance video delivery efficiency while maintaining high Quality of Service (QoS) and Quality of Experience (QoE) through effective management of network resources such as bandwidth, jitter, and latency. Leveraging Ant Colony Optimization (ACO), MPTP-VS establishes an adaptive multi-path routing strategy that dynamically discovers network topology and promptly transmits data, ensuring seamless video streaming without prior knowledge of the network topology. The protocol’s performance was evaluated using a series of experiments within an Omnet++ simulation environment, focusing on video streaming in Vehicular Fog Computing (VFC) environments during road incidents. Key performance metrics, including latency, response time, path discovery time, and throughput, were compared against those of Ad Hoc On-Demand Distance Vector (AODV) and Dynamic MANET On-demand (DYMO) protocols. The evaluation of QoE included the analysis of visual quality under conditions of rapid vehicle movement, measuring received quality, Peak Signal to Noise Ratio (PSNR), and resolution. Additionally, the QoE performance of MPTP-VS was compared with current Content Delivery Network (CDN) systems employing Adaptive Bitrate Streaming (ABS), which dynamically adjusts video quality according to network conditions. The findings demonstrate the effectiveness of the proposed protocol and its potential to significantly enhance video streaming performance in vehicular environments.

The concept of data dissemination has become an inherent problem with Vehicular Ad hoc NETwork (VANET), especially when traffic jams occur. The main idea of data dissem-ination is how to handle a huge amount of traffic data collected in the vicinity. Therefore, different strategies have been proposed by the research community in this field to solve this problem, including routing protocols, virtual machines, data aggregation, clustering and so on. In this paper, a virtual cluster strategy is proposed to select candidate vehicles acting as getaways in the network. In particular, the formation of the virtual clusters is based on dominant set algorithm to create virtual network for data dissemination. In the evaluation phase, the simulation results demonstrate the effectiveness of the proposed clustering strategy to significantly improve network performances.

This article explores a challenge related to managing and forecasting short-term traffic flows. The accuracy of this prediction is the subject of much research because it directly affects the performance of many traffic applications. In particular, ensuring the monotonicity of traffic variables is predominant for accuracy. In this paper, a predictive model-based Fuzzy Traffic Data Fusion (FTD F) system is proposed to predict traffic based on multiple sources. The model incorporates fitted features via fuzzy integral and Multiple Linear Regression (MLR) to ensure monotonicity by assigning importance weights to feature sets. The evaluation step is based on analyzed traffic data, the results of which show a high approximation rate of the predicted data compared to the real data and the prediction error is significantly reduced. Additionally, the performance outperforms existing predictive models using the prediction criteria.