Improving the QoE of DASH over SDN: A MCDM Method with an Intelligent Approach

As being one of the most popular applications in the last decade, dynamic adaptive video streaming applications are used by Internet users every day. In such applications, the underlying architecture allows users to change quality adaptively as their request. The purpose of quality or rate adaptation algorithm is to achieve highest QoE possible. In this paper, we propose a rate adaptation algorithm which allows to increase the quality of already buffered video by using Multi-Criteria Decision Making (MCDM) method. Increasing the quality of the buffered video can be beneficial in areas from resiliency to entertainment. We propose to utilize SDN for deciding weights of MCDM method. For this purpose, SDN controller runs a machine learning algorithm by using its knowledge about current network conditions as an input of the learning algorithm. Simulation results show that users achieve higher QoE by using our approach when compared to conventional rate adaptation algorithm.

[1]  Christian Timmerer,et al.  A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP , 2019, IEEE Communications Surveys & Tutorials.

[2]  Müge Sayit,et al.  An SDN-assisted system design for improving performance of SVC-DASH , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[3]  Bo Wang,et al.  Improving Optimization-Based Rate Adaptation in DASH System , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).

[4]  Thomas Stockhammer,et al.  Dynamic adaptive streaming over HTTP --: standards and design principles , 2011, MMSys.

[5]  Igor Radusinovic,et al.  SDN control framework for QoS provisioning , 2014, 2014 22nd Telecommunications Forum Telfor (TELFOR).

[6]  Filip De Turck,et al.  Optimizing scalable video delivery through OpenFlow layer-based routing , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[7]  Thomas Wiegand,et al.  iDASH: improved dynamic adaptive streaming over HTTP using scalable video coding , 2011, MMSys.

[8]  Edmundas Kazimieras Zavadskas,et al.  Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014 , 2015, Expert Syst. Appl..

[9]  Pedro Casas,et al.  Improving QoE prediction in mobile video through machine learning , 2017, 2017 8th International Conference on the Network of the Future (NOF).

[10]  Daniel Zappala,et al.  Quality selection for Dynamic Adaptive Streaming over HTTP with Scalable Video Coding , 2012, MMSys '12.

[11]  Ke Liu,et al.  Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding , 2018, IEEE Transactions on Mobile Computing.

[12]  Chia-Wen Lin,et al.  mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming , 2016, IEEE Transactions on Multimedia.

[13]  Arjan Durresi,et al.  Quality of Service (QoS) in Software Defined Networking (SDN): A survey , 2017, J. Netw. Comput. Appl..

[14]  Müge Sayit,et al.  Rate adaptation algorithm with backward quality increasing property for SVC-DASH , 2017, 2017 IEEE 7th International Conference on Consumer Electronics - Berlin (ICCE-Berlin).

[15]  Pradipta De,et al.  Candid with YouTube: Adaptive Streaming Behavior and Implications on Data Consumption , 2017, NOSSDAV.

[16]  Bruno Sinopoli,et al.  A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP , 2015, Comput. Commun. Rev..