Jesús-Azabal, M.; Soares, V.N.G.J.; Galán-Jiménez, J. ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach. Electronics2024, 13, 1569.
Jesús-Azabal, M.; Soares, V.N.G.J.; Galán-Jiménez, J. ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach. Electronics 2024, 13, 1569.
Jesús-Azabal, M.; Soares, V.N.G.J.; Galán-Jiménez, J. ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach. Electronics2024, 13, 1569.
Jesús-Azabal, M.; Soares, V.N.G.J.; Galán-Jiménez, J. ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach. Electronics 2024, 13, 1569.
Abstract
Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions becomes a challenge due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts where an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live-video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal has been implemented and assessed in a laboratory context, analyzing the model performance and QoS metrics. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% of accuracy. Also, obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.
Keywords
Mobile Adhoc Networks; Live-video streaming; Artificial Intelligence; Machine Learning; Bluetooth Low Energy; Offline streaming
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
