Adaptive Quality Control Scheme to Improve QoE of Video Streaming in Wireless Networks

  • Minsu Kim
  • Kwangsue ChungEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)


Recently, with the spread of smart devices and the development of networks, the demand for video streaming has increased, and HTTP adaptive streaming has been gaining attention. HTTP adaptive streaming can guarantee QoE (Quality of Experience) because it selects the video quality according to the network state. However, in wireless networks, delay and packet loss rates are high and the available bandwidth fluctuates sharply. Therefore, QoE is degraded when the quality is selected on the basis of the measured bandwidth. In this paper, we propose an adaptive quality control scheme to improve QoE of video streaming in wireless networks. The proposed scheme calculates two factors, the buffer underflow probability and the instability, by considering the buffer state and the changes of quality level. Using these factors, the proposed scheme defines a quality control region that consists of four sub-regions. The video quality is determined by applying different control strategy to each sub-region. The results of experiments have shown that the proposed scheme improves QoE compared to the existing quality control schemes by minimizing the buffer underflow and the unnecessary quality changes and maximizing the average video quality.


HTTP adaptive streaming Quality of Experience Wireless networks Instability Buffer underflow probability 



This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00224, Development of generation, distribution and consumption technologies of dynamic media based on UHD broadcasting contents).


  1. 1.
    Cisco Visual Networking Index: Forecast and Methodology, 2016–2021. Cisco System Inc., San Jose, June 2017Google Scholar
  2. 2.
    Kua, J., Armitage, G., Branch, P.: A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Commun. Surv. Tutorials 19(3), 1842–1866 (2017)CrossRefGoogle Scholar
  3. 3.
    Aguayo, M., Bellido, L., Lentisco, C.M., Pastor, E.: DASH adaptation algorithm based on adaptive forgetting factor estimation. IEEE Trans. Multimed. 20(5), 1224–1232 (2017)CrossRefGoogle Scholar
  4. 4.
    Bedogni, L., et al.: Dynamic adaptive video streaming on heterogeneous TVWS and Wi-Fi networks. IEEE/ACM Trans. Netw. 25(6), 3253–3266 (2017)CrossRefGoogle Scholar
  5. 5.
    Li, Z., et al.: Probe and adapt: rate adaptation for HTTP video streaming at scale. IEEE J. Sel. Areas Commun. 32(4), 719–733 (2014)CrossRefGoogle Scholar
  6. 6.
    Huang, T., Johari, R., McKeown, N., Trunnell, M., Waston, M.: A buffer-based approach to rate adaptation: evidence from a large video streaming service. In: Proceedings of the ACM Conference on SIGCOMM, pp. 187–198, August 2014Google Scholar
  7. 7.
    Miller, K., Bethanabholta, D., Caire, G., Wolisz, A.: A control-theoretic approach to adaptive video streaming in dense wireless networks. IEEE Trans. Multimed. 17(8), 1309–1322 (2015)CrossRefGoogle Scholar
  8. 8.
    Chen, S., Yang, J., Ran, Y., Yang, E.: Adaptive layer switching algorithm based on buffer underflow probability for scalable video streaming over wireless networks. IEEE Trans. Circ. Syst. Video Technol. 26(6), 1146–1160 (2016)CrossRefGoogle Scholar
  9. 9.
    Tian, G., Liu, Y.: On adaptive HTTP streaming to mobile devices. In: Proceedings of the International Packet Video Workshop, pp. 1–8, December 2013Google Scholar
  10. 10.
    The Network Simulator NS-3. <>

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringKwangwoon UniversitySeoulSouth Korea

Personalised recommendations