Direct QoE Measurement of Real-Time H.264 Streaming in OLSR-Based MANETs: An ns-3 and FFmpeg Experimental Framework
DOI:
https://doi.org/10.56714/bjrs.51.2.21Keywords:
Congestion Control, Active Queue Management, BLUE Algorithm, Deep Reinforcement Learning, DQN, Network SimulationAbstract
Mobile Ad Hoc Networks (MANETs) are infrastructure-less wireless networks in which mobile nodes communicate through multi-hop links. Supporting real-time video transmission in such networks is challenging due to node mobility, dynamic topology, limited bandwidth, and frequent route changes. Among proactive routing protocols, the Optimized Link State Routing (OLSR) protocol is widely used because of its low route acquisition delay, which makes it suitable for delay-sensitive multimedia applications.
In this study, a complete experimental evaluation of real-time video transmission over an OLSR-based MANET is presented using the ns-3.41 network simulator under Ubuntu Linux. A pre-encoded H.264 video stream was transmitted over progressively larger network scenarios, starting from small topologies and extending to a dynamic 25-node MANET using the Random Waypoint mobility model. Video quality assessment was performed directly on the received video files using FFmpeg-based tools, without relying on the Evalvid framework.
Both Quality of Service (QoS) and Quality of Experience (QoE) metrics were analyzed. The results show that OLSR is capable of delivering real-time video with acceptable visual quality in moderately dense MANET scenarios, achieving average Peak Signal-to-Noise Ratio) PSNR( values of approximately 29.6 dB, Structural Similarity Index)SSIM( values between 0.93 and 0.95, and Mean Opinion Score)MOS( scores ranging from 3.5 to 3.9. This work provides a validated experimental baseline for future research on QoE-aware optimization and adaptive video transmission in MANET environments
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