QoE-Oriented Open Radio Access Networks for Virtual Reality Applications

Kougioumzidis, Georgios; Vlahov, Atanas; Poulkov, Vladimir; Zaharis, Zaharias D.; Lazaridis, Pavlos I.

doi:10.1109/wpmc55625.2022.10014946

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…Therefore, the network/transmission parameters of latency, bandwidth, and packet loss play an important role in maintaining immersive experiences. Because of the limitations of human perception, a procedure for mapping these parameters to attributes that directly affect the perceived quality of VR applications by end-users can be implemented [28].…”

Section: Network/transmission Influencing Factorsmentioning

confidence: 99%

Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

Kougioumtzidis,

Vlahov,

Poulkov

et al. 2023

IEEE Access

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Nowadays, innovative applications in the field of virtual reality (VR) are being developed, attracting the interest of both academia and industry. Wireless VR applications focus on various aspects of daily life, such as smart education, entertainment, healthcare, tourism, architecture, automotive, and industrial automation. All these inherently interactive applications that aim to create immersive experiences for users are closely related to the concept of quality of experience (QoE), which expresses the quality of a service as perceived by end-users. In this paper, we develop an objective QoE prediction model based on deep learning techniques. The prediction model examines the impact of wireless network operation on the quality of VR 360-degree video streaming. It is based on an encoder-decoder long short-term memory (LSTM) neural network and is able to predict in real-time the overall transmission-related QoE value using only measurable quality of service (QoS) parameters. The prediction model is tested and evaluated on an open radio access network testbed with interfaces based on the O-RAN specifications. INDEX TERMSDeep learning, deep neural network (DNN), long short-term memory (LSTM), open radio access network (Open RAN), quality of experience (QoE), QoE prediction, recurrent neural network (RNN), virtual reality (VR).

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Section: Network/transmission Influencing Factorsmentioning

confidence: 99%

Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

Kougioumtzidis,

Vlahov,

Poulkov

et al. 2023

IEEE Access

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“…Firouzi et al [35] adopted O-RAN to optimize federated learning (FL) operation in supporting distributed edge intelligence of 5G-based IoT systems. Liu et al [52] addressed network slicing security in cyber-physical systems (CPS), while Kougioumzidis et al [53] considered extending O-RAN capabilities to virtual reality applications. Vila et al [54] presented the network digital twins (DT) concept to reflect the RAN's operation for taking the appropriate action.…”

Section: Brief Review On O-ranmentioning

confidence: 99%

Open Radio Access Networks for Smart IoT Systems: State of Art and Future Directions

Musa,

Hussaini,

Qian

et al. 2023

Future Internet

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The Internet of Things (IoT) constitutes a vast network comprising various components such as physical devices, vehicles, buildings, and other items equipped with sensors, actuators, and software. These components are interconnected, facilitating the collection and exchange of copious data across networked communications. IoT empowers extensive monitoring and control over a myriad of objects, enabling them to gather and disseminate data that bolster applications, thereby enhancing the system’s capacity for informed decision making, environmental surveillance, and autonomous inter-object interaction, all without the need for direct human involvement. These systems have achieved seamless connectivity requirements using the next-generation wireless network infrastructures (5G, 6G, etc.), while their diverse reliability and quality of service (QoS) requirements across various domains require more efficient solutions. Open RAN (O-RAN), i.e., open radio open access network (RAN), promotes flexibility and intelligence in the next-generation RAN. This article reviews the applications of O-RAN in supporting the next-generation smart world IoT systems by conducting a thorough survey. We propose a generic problem space, which consists of (i) IoT Systems: transportation, industry, healthcare, and energy; (ii) targets: reliable communication, real-time analytics, fault tolerance, interoperability, and integration; and (iii) artificial intelligence and machine learning (AI/ML): reinforcement learning (RL), deep neural networks (DNNs), etc. Furthermore, we outline future research directions concerning robust and scalable solutions, interoperability and standardization, privacy, and security. We present a taxonomy to unveil the security threats to emerge from the O-RAN-assisted IoT systems and the feasible directions to move this research forward.

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QoE-Oriented Open Radio Access Networks for Virtual Reality Applications

Cited by 2 publications

References 20 publications

Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

Open Radio Access Networks for Smart IoT Systems: State of Art and Future Directions

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