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QoE-Driven Resource Allocation for Live Video Streaming Over D2D-Underlaid 5G Cellular Networks
Author(s) -
Jihyeok Yun,
Md. Jalil Piran,
Doug Young Suh
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2882441
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Recently, device-to-device (D2D)-underlaid fifth-generation (5G) cellular networks have received plenty of attention because of their ability to save network resources and to reduce energy consumption. Most existing algorithms for multimedia services over D2D networks consider only the signal-to-noise ratio (SNR) and ignore temporal requirements, which do not provide optimum performances. To overcome this issue, we propose a framework for a cross-layer D2D link control system, which guarantees the quality of service and improves the quality of experience (QoE) for live video streaming with different priorities and delay constraints. In this framework, we considered three techniques, including priority-based video transmission, flexible communication mode switching of user equipment, and subset-based relay assignment. According to the live video generation period, our system dynamically adjusts the ratio between cellular and D2D mode durations in each unit of the communication period for each user individually. Our proposal also considerably reduces the duration and frequency of video playback freezing by delivering at least the minimum service quality of the delivered video to the sink users even in the shadow area and therefore improves the QoE for all users while minimizing energy consumption. System-level simulation shows that the proposed algorithm outperforms other methods in terms of the average mean time to failure, average peak SNR, and average energy consumption.

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