Social-Aware Caching and Resource Sharing Optimization for Video Delivering in 5G Networks

Bui, Minh-Phung; Vo, Nguyen-Son; Nguyen, Tien‐Thanh; Tran, Quang-Nhat; Tran, Anh-Tuan

doi:10.1007/978-3-030-14413-5_6

Cited by 3 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The low-level design considerations will be debated and illustrated in forthcoming sections. Socialawareness, [133] mobility patterns [127], and user preferences [14] are input to an ML framework that can be federated among multiple nodes, hosted at distributed MEC servers, C-RAN, or centralized CDN. The intelligent and optimal decisions are fed-back to the caches that are managed by network virtualization techniques [89], [134].…”

Section: State-of-the-art: Machine Learning Based Cachingmentioning

confidence: 99%

“…Researchers [133], [167] proposed a social-aware caching and resource sharing scheme in 5G networks. The objective of the study was to lower the burden on backhaul links and increase system delivery capacity.…”

Section: A Social-aware Caching In Edge Networkmentioning

confidence: 99%

“…ML techniques perform better in complex wireless networks considering the dynamics of user mobility and content popularity. Most of the social-aware edge caching proposals do not include mobility and popularity prediction techniques which can further enhance the system performance and accuracy [133]. Popularity prediction techniques often miss opportunities in the application of ML techniques [38].…”

Section: Lessons Learned: Summary and Insightsmentioning

confidence: 99%

“…Wu et al [35] comprehensively define a parameter named social tie strength which is dependent on the frequency of content sharing and similarity of cached content between two UE. On the other hand, Bui et al [133] model social relationships as the number of social encounters and the duration of the encounter. Wang et al [40] define social tie strength as contact time between two UEs.…”

Section: Comparison: Social-awareness Popularity Prediction and Commu...mentioning

confidence: 99%

See 3 more Smart Citations

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

View full text Add to dashboard Cite

Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. Edge networks are complex and dynamic. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (application and objective), (b) caching strategy (policy, location, and objective), and edge network (type and delivery strategy). We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.

show abstract

Section: State-of-the-art: Machine Learning Based Cachingmentioning

confidence: 99%

Section: A Social-aware Caching In Edge Networkmentioning

confidence: 99%

Section: Lessons Learned: Summary and Insightsmentioning

confidence: 99%

Section: Comparison: Social-awareness Popularity Prediction and Commu...mentioning

confidence: 99%

See 2 more Smart Citations

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…, I N }, where I i ∈ [0, 1] and N ∈ N [22,23]. For physical attributes, we calculated Cumulative closeness coefficient between by assuming Γ = 20 s, and e i=1 t i(Tx, Rx) ∈ [0, N] and N ∈ N [24]. Similarly, D was calculated based on the polar coordinates of uniformly distributed UEs in the coverage area using (5) [17].…”

Section: Social Friendship Indexmentioning

confidence: 99%

Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks

Nadeem

Cho

2019

Electronics

View full text Add to dashboard Cite

Device-to-device communications refer to the emerging paradigm that permits direct communication between cellular users that are in close physical and social proximity. It is expected to become an essential aspect of the future 5G cellular communications system. As a prerequisite for device-to-device communications, a user has to select another user in its proximity that has the desired information/service and is willing to share it. In this paper, we propose a method for peer selection in dense small-cell device-to-device networks that uses multi-attribute decision modeling. The method exploits both the physical and social characteristics of the user equipment to find the most suitable peer for device-to-device communications. We assume hexagonal small-cell and macro-cell architectures with a small-cell/macro-cell base station with multiple user equipments in its coverage area to evaluate the proposed scheme’s performance. The small-/macro-cell base station exploits various social and physical attributes to rank peers and selects the best one for device-to-device communication with the requesting user. The numerical results demonstrate the proposed algorithm’s efficiency in terms of computational time, selection of the best peer, throughput, and energy efficiency of device-to-device communications.

show abstract

Resource Sharing and Segment Allocation Optimized Video Streaming over Multi-hop Multi-path in Dense D2D 5G Networks

Tran

Bui

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Social-Aware Caching and Resource Sharing Optimization for Video Delivering in 5G Networks

Cited by 3 publications

References 25 publications

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks

Resource Sharing and Segment Allocation Optimized Video Streaming over Multi-hop Multi-path in Dense D2D 5G Networks

Contact Info

Product

Resources

About