The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
360° Mulsemedia Experience over Next Generation Wireless Networks - A Reinforcement Learning Approach
Abstract
The next generation of wireless networks targets aspiring key performance indicators, like very low latency, higher data rates and more capacity, paving the way for new generations of video streaming technologies, such as 360° or omnidirectional videos. One possible application that could revolutionize the streaming technology is the 360° MULtiple SEnsorial MEDIA (MULSEMEDIA) which enriches the 360° video content with other media objects like olfactory, haptic or even thermoceptic ones. However, the adoption of the 360° Mulsemedia applications might be hindered by the strict Quality of Service (QoS) requirements, like very large bandwidth and low latency for fast responsiveness to the user's inputs that could impact their Quality of Experience (QoE). To this extent, this paper introduces the new concept of 360° Mulsemedia as well as it proposes the use of Reinforcement Learning to enable QoS provisioning over the next generation wireless networks that influences the QoE of the end-users.
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The performance of the 5G radio scheduler is shown to depend on the number of 360 mulsemedia users, mobility, positioning and channel conditions.
“The performance of the 5G radio scheduler is shown to depend on the number of 360 mulsemedia users, mobility, positioning and channel conditions.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [320]
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Cites
Optimizing 360 video delivery over cellular networks
Qian, F., Ji, L., Han, B., & Gopalakrishnan, V. (2016) · Proceedings of the 5th Workshop on All Things Cellular: Operations, Applications and Challenges · doi:10.1145/2980055.2980056 · Cited by 402
Viewport-adaptive navigable 360-degree video delivery
Corbillon, X., Simon, G., Devlic, A., & Chakareski, J. (2017) · 2017 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2017.7996611 · Cited by 341
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QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Barakabitze, A. A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M. G., & Atzori, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2958784 · Cited by 239
Metadata
- Authors
- Comsa, I.-S., Trestian, R., & Ghinea, G.
- Affiliations
- Middlesex University London (GB)
- ORCID
- Trestian, Ramona
- Venue
- 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 1-6
- OpenAlex
- W2795045038
- Cited by
- 30
- References
- 2
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 5 · 2021: 1 · 2020: 3 · 2019: 13 · 2018: 2
- FWCI
- 1.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Quality of Multimedia Experience (QoMEX) (publishedVersion)
- Middlesex University Research Repository (Middlesex University Of London) (acceptedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion)
- Funders
- European Commission (HORIZON2020, 688503)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Engineering › Media Technology › Multimedia Communication and Technology
- Keywords
- 360-degree video streaming, multisensory media, next-generation wireless networks, quality of service provisioning, quality of experience, reinforcement learning, low latency, bandwidth requirements
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