QoE-Assured Live Streaming via Satellite Backhaul in 5G Networks

Ge, Chang; Wang, Ning; Selinis, Ioannis; Cahill, Joe; Kavanagh, Mark; Liolis, Konstantinos; Politis, Christos; Nunes, J.M.B.; Evans, B.G.; Rahulan, Yogaratnam; Nouvel, Nivedita; Boutin, Mael; Desmauts, Jeremy; Arnal, Fabrice; Watts, Simon; Poziopoulou, Georgia

doi:10.1109/tbc.2019.2901397

Cited by 68 publications

(53 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5G technology features high speed, high capacity, low power consumption, low delay, and high reliability [20]. It is the main development direction of mobile Internet and IoT, which can fully meet the bandwidth, delay, reliability, security, and mobility requirements of the V2G system [21].…”

Section: The User Layermentioning

confidence: 99%

EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

Shen

Fang

et al. 2020

Electronics

View full text Add to dashboard Cite

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select an EV suitable for scheduling. In order to improve the efficiency of scheduling, we first need to determine define categories of target EV users. We found that grouping on the basis of EV charging behavior is one effective method to identify target EVs. Therefore, we propose a hybrid artificial intelligence classification method based on the charging behavior profile of EVs. Through this classification method, target EVs can be accurately identified. The results of cross-validation experiments and performance evaluations suggest that this method is effective.

show abstract

Section: The User Layermentioning

confidence: 99%

EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

Shen

Fang

et al. 2020

Electronics

View full text Add to dashboard Cite

show abstract

“…This is a joint demonstrator developed by 5G-Xcast & SaT5G 5G-PPP projects involving Avanti's high throughput HYLAS 4 geostationary orbit (GEO) satellite capacity, Broadpeak's MEC-enabled platform for the content delivery network, University of Surrey's 5GIC testbed network and VT iDirect's 5G enabled satellite hub platform and satellite terminals [20] [40]. This demonstrator focuses on using satellite multicast capabilities to deliver live channels to a 5G edge mobile network.…”

Section: Joint Demonstrator With Sat5g: Optimized Resources Allocamentioning

confidence: 99%

Demonstrating Immersive Media Delivery on 5G Broadcast and Multicast Testing Networks

Eyles

Jokela

et al. 2020

IEEE Trans. on Broadcast.

View full text Add to dashboard Cite

This work presents eight demonstrators and one showcase developed within the 5G-Xcast project. They experimentally demonstrate and validate key technical enablers for the future of media delivery, associated with multicast and broadcast communication capabilities in 5 th Generation (5G). In 5G-Xcast, three existing testbeds: IRT in Munich (Germany), 5GIC in Surrey (UK), and TUAS in Turku (Finland), have been developed into 5G broadcast and multicast testing networks, which enables us to demonstrate our vision of a converged 5G infrastructure with fixed and mobile accesses and terrestrial broadcast, delivering immersive audiovisual media content. Built upon the improved testing networks, the demonstrators and showcase developed in 5G-Xcast show the impact of the technology developed in the project. Our demonstrations predominantly cover use cases belonging to two verticals: Media & Entertainment and Public Warning, which are future 5G scenarios relevant to multicast and broadcast delivery. In this paper, we present the development of these demonstrators, the showcase, and the testbeds. We also provide key findings from the experiments and demonstrations, which not only validate the technical solutions developed in the project, but also illustrate the potential technical impact of these solutions for broadcasters, content providers, operators, and other industries interested in the future immersive media delivery.

show abstract

“…The state-of-the-art communication technology 5G not only relies on the expanded bandwidth to support the applications of video services with quality-increasing visual content, but also employs the ultra-dense deployment to provides adequate air interface for the explosive growth of mobile devices in order to deal with the decimated coverage [1]- [7]. The The associate editor coordinating the review of this manuscript and approving it for publication was Ayaz Ahmad .…”

Section: Introductionmentioning

confidence: 99%

Social-Aware D2D Video Delivery Method Based on Mobility Similarity Measurement in 5G Ultra-Dense Network

Zhang

Jia

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The huge amounts of network traffic generated by the ultra-high-definition video playback of super-large-scale video users results in tremendous pressure for front-haul and back-haul in the 5G network, which brings severely negative influence for the large-scale deployment and scalability of video systems and video delivery performance (e.g. transmission delay and packet loss) related to user quality of experience. The direct D2D communications between mobile devices with adjacent position in geographical area can offloading huge video traffic into the underlying networks, which reduces load of cellular base station in the edge networks and relieves traffic pressure in the core networks. In this paper, we propose a novel Social-Aware D2D Video Delivery Method based on Rapid Sample-Efficient Measurement of Mobility Similarity in 5G Ultra-Dense Network (DMSEM). By investigation for one-hop D2D pair relationship, DMSEM builds a social state transition model of user movement, which makes use of encounter duration and shared video length between encountered users to define the state transition condition. A cluster algorithm of encounter events is proposed, which achieves initial clusters of encounter events by calculating similarity between encounter events from the two aspects of both variation rate of geographical distance between mobile users and encounter duration time. DMSEM makes use of the Fuzzy C-Means to refine the initial clusters and extracts encounter patterns of mobile users. DMSEM designs a sample-efficiency rapid recognition algorithm of encounter pattern, which can use small number of encounter distance samples to achieve fast heuristic recognition of encounter pattern. Extensive tests show how DMSEM achieves better results in comparison with other state-of-the-art solutions in terms of packet loss rate, average freeze time, cache utilization, average bitrate, buffer level and control overhead. INDEX TERMS Social-aware, video delivery, mobility similarity, 5G ultra-dense network, traffic offloading.

show abstract

QoE-Assured Live Streaming via Satellite Backhaul in 5G Networks

Cited by 68 publications

References 9 publications

EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

Demonstrating Immersive Media Delivery on 5G Broadcast and Multicast Testing Networks

Social-Aware D2D Video Delivery Method Based on Mobility Similarity Measurement in 5G Ultra-Dense Network

Contact Info

Product

Resources

About