360° Mulsemedia Experience over Next Generation Wireless Networks - A Reinforcement Learning Approach

Comsa, Loan-Sorin; Trestian, Ramona; Ghinea, Gheorghiţă

doi:10.1109/qomex.2018.8463409

Cited by 24 publications

(13 citation statements)

References 23 publications

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“…Much akin to the case of the tactile Internet, 360º video applications could also benefit from the same principle to improve the overall QoE performance, regardless of other factors such as dynamic network conditions. In this context, we introduce a new application type entitled 360º mulsemedia 8 . In these applications, the 360º media is enriched with additional sensory effects such as haptic/wind, heat or smell.…”

Section: Do I Smell Coffee? the Tale Of A 360º Mulsemedia Experiencementioning

confidence: 99%

Do I Smell Coffee? The Tale of a 360° Mulsemedia Experience

et al. 2019

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Section: Do I Smell Coffee? the Tale Of A 360º Mulsemedia Experiencementioning

confidence: 99%

Do I Smell Coffee? The Tale of a 360° Mulsemedia Experience

et al. 2019

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“…A dataset of videos enriched with olfactory content and annotated with subjective user ratings are also provided. A new concept of 360 • mulsemedia as the application that will revolutionize the streaming technology is introduced by Comsa et al [320]. Fig.…”

Section: A Qoe In Immersive Ar/vr and Mulsemedia Applicationsmentioning

confidence: 99%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

168

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The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers' needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a highlevel description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoEoriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

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“…In Reference [31], Actor-Critic Learning Automata (ACLA) RL algorithm is used to learn the scheduling rule to be applied at each TTI in order to meet the delay, packet loss, and bit rate requirements for different traffic classes. The same actor-critic algorithm is employed in Reference [32] to optimize the scheduling problem for 360 • mulsemedia traffic class (additional human senses are incorporated alongside conventional 360 • video content) that is characterized by very high data rates and very low delay requirements. ACLA algorithm is not always the best option when optimizing the delay and packet loss objectives for various parameter settings, as stated in Reference [33].…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Reinforcement Learning Algorithms in Fairness-Oriented OFDMA Schedulers

et al. 2019

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Due to large-scale control problems in 5G access networks, the complexity of radioresource management is expected to increase significantly. Reinforcement learning is seen as apromising solution that can enable intelligent decision-making and reduce the complexity of differentoptimization problems for radio resource management. The packet scheduler is an importantentity of radio resource management that allocates users’ data packets in the frequency domainaccording to the implemented scheduling rule. In this context, by making use of reinforcementlearning, we could actually determine, in each state, the most suitable scheduling rule to be employedthat could improve the quality of service provisioning. In this paper, we propose a reinforcementlearning-based framework to solve scheduling problems with the main focus on meeting the userfairness requirements. This framework makes use of feed forward neural networks to map momentarystates to proper parameterization decisions for the proportional fair scheduler. The simulation resultsshow that our reinforcement learning framework outperforms the conventional adaptive schedulersoriented on fairness objective. Discussions are also raised to determine the best reinforcement learningalgorithm to be implemented in the proposed framework based on various scheduler settings.

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360° Mulsemedia Experience over Next Generation Wireless Networks - A Reinforcement Learning Approach

Cited by 24 publications

References 23 publications

Do I Smell Coffee? The Tale of a 360° Mulsemedia Experience

Do I Smell Coffee? The Tale of a 360° Mulsemedia Experience

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

A Comparison of Reinforcement Learning Algorithms in Fairness-Oriented OFDMA Schedulers

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