# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
## 9 Conclusions and Future Work Direction  
This paper addressed the challenge of delivering 4K video streams over multipath networks, focusing on streaming high-quality video through the MPT-GRE multipath network layer solution. The MPT-GRE tunnel achieves a throughput that closely approximates the combined throughput of its physical paths. Our results demonstrate that the MPT-GRE multipath network is efficient and effective for real-time 4K video streaming. An analysis of key quality metrics SSIM, MSE, and PSNR confirmed that the throughput aggregation of the MPT-GRE tunnel can significantly enhance streaming quality in real time, particularly for high-resolution videos. Streaming through a single path led to noticeable decreases in SSIM, PSNR values, and increases in MSE, indicating reduced video quality. In contrast, video streamed through the MPT-GRE tunnel exhibited only slight variations in these metrics, with SSIM, MSE, and PSNR values often nearly identical to those of the original pre-streamed video. These findings show that the MPT-GRE tunnel consistently outperforms single-path transmission in maintaining superior video quality. It is especially valuable for applications like live streaming, online gaming, and video conferencing, where consistent, high-quality video delivery is essential. The MPT-GRE tunnel offers a reliable solution for platforms requiring high-performance video across diverse and unstable network conditions.  
Some of the future research directions include calculating more advanced metrics, such as Video Multi-Method Assessment Fusion (VMAF) for 8K video streaming and integrating with 5G.  
All authors contributed to the study's conception and design. Material preparation and testbed building by [PERSON]-I., with valuable support from [PERSON]. The first draft of the manuscript was written by N.A.-I., and all authors commented on previous versions of the manuscript. Conceptualization: G.L.; methodology: N.A.-I.; formal analysis and investigation: N.A.-I.; writing--original draft preparation: [PERSON]-I.; writing--review and editing: [PERSON] and N.A.-I.; resources: N.A.-I. and G.L.; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The Public GitHub repository of the first author contains several configuration settings files of MPT and scripts that were used to build the experiments: [[https://github.com/NaseerAlabbar/MPT_Connections_files](https://github.com/NaseerAlabbar/MPT_Connections_files)]([https://github.com/NaseerAlabbar/MPT_Connections_files](https://github.com/NaseerAlabbar/MPT_Connections_files)) (accessed on 28 March 2023). The Python script to calculate the average SSIM, MSE, and PSNR values for recorded video files: [[https://github.com/NaseerAlabbar/4k_video_metric](https://github.com/NaseerAlabbar/4k_video_metric)]([https://github.com/NaseerAlabbar/4k_video_metric](https://github.com/NaseerAlabbar/4k_video_metric)) (accessed on 3 November 2024).  
The authors thank [PERSON] from Szechenyi Istvan University for proof-reading the English-language version of the manuscript.  
The authors declare no conflict of interest.