QoE-driven DASH multicast scheme for 5G mobile edge network

被引：3
作者：
Tan X. [1 ]
Xu L. [1 ]
Zheng Q. [1 ]
Li S. [1 ]
Liu B. [2 ]
机构：
[1] Laboratory for Future Networks, the University of Science and Technology of China, Hefei
[2] The 54th Research Institute of China Electronic Technology Corporation, Shijiazhuang
来源：
Journal of Communications and Information Networks | 2021年 / 6卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Adaptive bitrate algorithm; DASH; Mobile edge computing; Multicast; Quality of experience;
D O I：
10.23919/JCIN.2021.9475125
中图分类号：
学科分类号：
摘要：
Dynamic adaptive streaming over HTTP (DASH) can adaptively select the appropriate video bitrate for mobile users. Mobile edge computing (MEC) scenario is of great benefit to improve the performance of mobile networks by providing computing and storage capabilities. And the utilization of spectrum resources can be improved by multicast transmission, but the performance of the multicast transmission will be directly affected by the selected grouping algorithm and resource allocation algorithm. In order to improve the quality of experience (QoE) of video users in the 5G MEC scenario, this paper proposes a QoE-driven DASH multicast scheme, which mainly covers the grouping algorithm and the adaptive bitrate (ABR) algorithm. First of all, we take the optimized target QoE as the basis for grouping and propose an adaptive grouping algorithm that can dynamically adjust the grouping results. Besides, we design a joint optimization ABR algorithm based on the prediction of QoE, which comprehensively considers the process of resource allocation and bitrate decision-making based on the prediction of QoE of video segments in a certain forward-looking field of view. The simulation results show that the proposed DASH multicast scheme performs well in QoE and fairness. © 2021, Posts and Telecom Press Co Ltd. All rights reserved.
引用
收藏
页码：153 / 165
页数：12
相关论文
共 37 条
  • [1] FORECAST G., Cisco visual networking index: global mobile data traffic forecast update, 2017-2022, Update, 2017, (2019)
  • [2] Minimum requirements related to technical performance for IMT-2020 radio interface(s), (2017)
  • [3] Study on scenarios and requirements for next generation access technologies v16.0.0, (2020)
  • [4] XU L, TAN X, LI S, Et al., A QoE-based DASH multicast grouping algorithm for mobile edge computing, 2020 IEEE/CIC International Conference on Communications in China, pp. 238-243, (2020)
  • [5] JAIN R K, CHIU D M W, HAWE W R., A quantitative measure of fairness and discrimination, (1984)
  • [6] TRAN T X, YUE G., GRAB: joint adaptive grouping and beamforming for multi-group multicast with massive MIMO, 2019 IEEE Global Communications Conference, (2019)
  • [7] CHEN J, CHIANG M, ERMAN J, Et al., Fair and optimal resource allocation for LTE multicast (eMBMS): group partitioning and dynamics, 2015 IEEE Conference on Computer Communications, pp. 1266-1274, (2015)
  • [8] LIU B, JIANG C, KUANG L, Et al., Joint user grouping and beamwidth optimization for satellite multicast with phased array antennas, 2020 IEEE Global Communications Conference, (2020)
  • [9] BANDI A, SHANKAR M R B, CHATZINOTAS S, Et al., Joint user grouping, scheduling, and precoding for multicast energy efficiency in multigroup multicast systems, IEEE Transactions on Wireless Communications, 19, 12, pp. 8195-8210, (2020)
  • [10] YANG S H, LIU T W., Quality control for hybrid unicast and multicast video transmission systems, 2020 IEEE International Conference on Consumer Electronics-Taiwan, (2020)
1234