Abstract
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.
Highlights
The state-of-the-art communication technology 5G relies on the expanded bandwidth to support the applications of video services with quality-increasing visual content, and 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 stability and durability of D2D communication pairs and uncertainty-tolerant level of movement behaviors are significant for the performance of social-aware D2D-based video sharing and user quality of experience (QoE)
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)
Summary
The state-of-the-art communication technology 5G relies on the expanded bandwidth to support the applications of video services with quality-increasing visual content, and 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 D2D communications between users with close relationship can achieve data exchange with high transmission rate and low energy consumption during a relatively long period time. The stability and durability of D2D communication pairs and uncertainty-tolerant level of movement behaviors are significant for the performance of social-aware D2D-based video sharing and user quality of experience (QoE). Because the closeness degree of social relationship does not ensure stability and durability of D2D communication distance between mobile devices, the dynamic movement behaviors of mobile devices brings severely negative influence for the performance D2D communications of the above methods. SOCIAL-BASED USER MOVEMENT STATE The D2D communications allow direct delivery of video data between nodes without the intervention of cellular tower, which reduces data forwarding load of cellular tower and enhances performance of data transmission. Gcj,i ≥ Smeans that the encounter duration time c=1 of nj and ni (the movement behaviors of nj and ni has enough stability) can support delivery of the whole video data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
