ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach

• Published in: Electronics (Basel) Vol. 13; no. 8; p. 1569
• Main Authors: Jesús-Azabal, Manuel, Soares, Vasco N. G. J., Galán-Jiménez, Jaime
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Algorithms; Analysis; Artificial intelligence; Bandwidths; Cloud computing; Communication; Decision analysis; Decision trees; Deep learning; Energy consumption; Forecasts and trends; Internet; Mobile ad hoc networks; Mobile devices; Multimedia; Multimedia communications; Network latency; Proposals; Quality; Quality of service; Software; Streaming media; Topology; Video compression; Video transmission
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13081569

Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions is challenging 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 in which 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 is implemented and assessed in a laboratory context, in which the model performance and QoS metrics are analyzed. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% accuracy. Also, the obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.