User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

Main Article Content

Muhamad Hanif Jofri
https://orcid.org/0000-0003-2980-083X
Ida Aryanie Bahrudin
Noor Zuraidin Mohd Safar
Juliana Mohamed
https://orcid.org/0000-0002-1621-2054
Abdul Halim Omar
https://orcid.org/0000-0001-7446-4885

Abstract

Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education,  and entertainment. However,   when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.

Article Details

How to Cite
1.
User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices. Baghdad Sci.J [Internet]. 2021 Dec. 20 [cited 2024 Dec. 23];18(4(Suppl.):1387. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6667
Section
article

How to Cite

1.
User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices. Baghdad Sci.J [Internet]. 2021 Dec. 20 [cited 2024 Dec. 23];18(4(Suppl.):1387. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6667

References

Boyce. J. M., Ye. Y., Chen. J., Ramasubramonian. A. K. (2016). Overview of SHVC: Scalable Extensions of the High Efficiency Video Coding Standard. IEEE Trans. Circuits. Syst.Video Technol. Vol: 26 , No 1. pp-201-34.

Chang. H., C., Agrawal. A., Cameron. K. (2011). Energy-Aware Computing for Android Platforms. International Conference Energy Aware Computing (ICEAC). Istanbul. pp. 1-4.

Corral. L., Georgiev. A. B., Sillitti. A., Succi. G. (2013). A Method for Characterizing Energy Consumption in Android Smartphones. 21st IEEE International Conference Green and Sustainable Software (GREENS). San Francisco, CA. pp. 38-45.

Devlic. A., Kamaraju. P., Lungaro. P., Segall. Z., Tollmar. K. (2015). QoE-aware optimization for video delivery and storage. 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

Ding. R., Muntean. G. M. (2013). Device Characteristics-based Differentiated Energy-efficient Adaptive Solution for Video Delivery over Heterogeneous Wireless Network. 2013 IEEE Wireless Communications and Networking Conference (WCNC). Shanghai. pp. 4588 – 4593.

Duanmu. Z., Rehman. A., Zeng. K., Wang. Z. (2016). Quality-Of-Experience Prediction For Streaming Video. 2016 IEEE International Conference on Multimedia and Expo (ICME).

Dutta. P., Seetharam. A., Arya. V., Chetlur. M., Kalyanaraman. S., Kurose. J. (2015). On managing quality of experience of multiple video streams in wireless networks. IEEE Transactions on Mobile Computing ( Volume: 14, Issue: 3, March 1 2015).

Fung. K. C., Kwok. Y. K. (2012). A QoE Based Performance Study of Mobile Peer-to-Peer Live Video Streaming. 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies.

Gao. X., Liu. D., Liu. D., Wang. H., Stavrou. A. (2017). E-Android: A New Energy Profiling Tool for Smartphones. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

Guillermo. L., Ballesteros. M., Ickin. S., Fiedler. M., Markendahl. J. (2016). Energy Saving Approaches for Video Streaming on Smartphone based on QoE Modeling. 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC).

Hossfeld. T., Seufert. M., Sieber. C., Zinner. T. (2014). Assessing Effect Sizes of Influence Factors Towards a QoE model for HTTP Adaptive Streaming. 2014 Sixth International Workshop on Quality of Multimedia Experience (QoMEX).

ITU-T. “P.910 : Subjective video quality assessment methods for multimedia applications.” In: ITU Rec. (2008).

Lee. S., Cha. H. (2017). User interface-level QoE analysis for Android application tuning. Perv. Mob. Comp. Vol. 40, pp. 382-396.

Petrangeli. S., Hooft. J. V. D., Wauters. T., Turck. F. D. (2018). Quality of Experience-Centric Management of Adaptive Video Streaming Services: Status and Challenges. ACM Transactions on Multimedia Computing, Communications, and Applications (Volume 14: Issue 2) May 2018.

Raheel. M. S., Iranmanesh. S., Raad. R. (2017). A novel Energy-Efficient Video Streaming method for decentralized Mobile Ad-hoc Networks. Perv. Mob. Comp. Vol. 40, pp 301-323.

Tarkoma. S., Sikkinen. M., Lagerspetz. E., Xiao. Y., (2014). Smartphone Energy Consumption: Modeling and Optimization. Publisher : University Printing House Cambridge Press, Online ISBN : 9781107326279.

Verdolini. A., Petrangeli. S. (2013). A Smartphone Agent For Qoe Evaluation And User Classification Over Mobile Networks. 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX).

Wang. H., Chen. B. (2013) Intrusion Detection System Based On Multi-Strategy Pruning Algorithm of the Decision Tree. Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS)

Xie. H., Shang. F. (2014). The Study of Methods for Post-pruning Decision Trees Based on Comprehensive Evaluation Standard. 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

Xiao. A., Liu. J., Li. Y., Song. Q., Ge. (2018). Two-phase rate adaptation strategy for improving real-time video QoE in mobile networks. China Com. Vol. 15 , No. 10.

Xu. Y., Elayoubi. S. E., Altman. E., El-Azouzi. R., Yu. Y. (2016). Flow-Level QoE of Video Streaming in Wireless Networks. IEEE Trans. Mob. Comp, Vol. 15, No. 11. pp- 2762 – 2780.

Yi. J., Luo. S., Yan. (2019). A measurement study of YouTube 360° live video streaming. NOSSDAV '19: Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video.

Similar Articles

You may also start an advanced similarity search for this article.