Machine Learning Concepts for Adaptive Video Streaming Systems (SS17)

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1 Machine Learning Concepts for Adaptive Video Streaming Systems (SS17) Kickoff Denny Stohr, M.Sc. Jeremias Blendin Prof. Dr.-Ing. Wolfgang Effelsberg Department of Computer Science Chair: Distributed Multimedia Systems Prof. Effelsberg 1

2 Organization 1/2 Assignment of Topics Please send an to your supervisor: Include your full name and Matrikelnummer! Select 3 topics and name your priorities! We can supervise a maximum 15 persons! Submission of your seminar plan Around one month after topic assignment the first mandatory submission is required! The plan includes: Planned references to be discussed Preliminary structure of your submission At least one summary of a paper! Important Dates: Kickoff Presentation: :50-10:50, S202 A213 Topic Registration Deadline (mandatory): :59:59 Deadline for Seminar Planning (mandatory): :59:59 Intermediate submission of full paper (no grading): Final paper/slides submission due: Presentation: :00 17:00 h Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 2

3 Organization 2/2 Paper Submission & Presentation 8 10 pages paper (IEEE formatting) Around 30 minutes presentation + 15 minutes of discussion Language: English or German! Presentation Slides should be English!!! Grading 60% Paper 30% Presentation 10% Contribution in final presentation session Warning: Plagiators will be prosecuted! Important Dates: Kickoff Presentation: :50-10:50, S202 A213 Topic Registration Deadline (mandatory): :59:59 Deadline for Seminar Planning (mandatory): :59:59 Intermediate submission of full paper (no grading): Final paper/slides submission due: Presentation: :00 17:00 h Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 3

4 Take a look at our webpage for more info On the course website you can find: Seminar topics and important dates Useful links and templates Our contact information The course website: Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 4

5 Dynamic Adaptive Streaming Over HTTP Seufert, M., Egger, S., Slanina, M., Zinner, T., Hossfeld, T., & Tran-gia, P. (2014). A Survey on Quality of Experience of HTTP Adaptive Streaming (Vol. 17) Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 5

6 Machine Learning Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 6

7 Topics Overview ID Topic Supervisor ML1 Machine Learning for DASH Adaptation Denny Stohr ML2 From QoS to QoE: QoE Models for Adaptive Streaming using Machine Learning Approaches Denny Stohr ML3 ML3: Understanding Video Quality using Machine Learning Denny Stohr TCP1 Multipath TCP for DASH Video Streaming Denny Stohr SDN1 Classification of SDN Forwarding Mechanisms Jeremias Blendin Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 7

8 ML1: Machine Learning for DASH Adaptation A key goal of DASH is to provide the best possible adaptation decisions for the given network context in order to maximize QoE. While most research focus on proposing new adaptation algorithms to do so, another way is to employ machine learning approaches for improved adaptation decisions. Your task is to provide an overview of current work proposing such strategies and discuss further machine learning concepts with regard to their usefulness in such applications. Y. L. Chien, K. C. J. Lin and M. S. Chen, "Machine learning based rate adaptation with elastic feature selection for HTTP-based streaming," 2015 IEEE International Conference on Multimedia and Expo (ICME), Turin, 2015, pp Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 8

9 ML2: From QoS to QoE: QoE Models for Adaptive Streaming using Machine Learning Approaches Understanding QoE in adaptive streaming applications such as DASH is complex and usually requires conducting subjective user studies within controlled test environments. However, crowd souring or monitored session information from video streaming providers result in large amounts of accessible data that can be used to understand such relations between QoS and QoE. Here, machine learning approaches are a well suited technique to understand such complex relations and predict QoE based on such data. The task for this seminar is to give an overview and categorize research conducted in this area. M. S. Mushtaq, B. Augustin and A. Mellouk, "Empirical study based on machine learning approach to assess the QoS/QoE correlation," th European Conference on Networks and Optical Communications, Vilanova i la Geltru, 2012, pp Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 9

10 ML3: Understanding Video Quality using Machine Learning Evaluating the visual quality of video streams or pictures can be categorized in two main areas: Full reference and no reference based metrics. While the first case relies on a direct comparison on the source and degraded frame, no reference metrics try to directly derive a quality measure using only the degraded frame. In this area, recent advances in machine learning, such as CNN S, promise to be a useful approach in this domain. The task in this seminar is give an overview and categorize research conducted in this area. Kang, Le, et al. "Convolutional neural networks for no-reference image quality assessment." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 10

11 TCP1: Multipath TCP for DASH Video Streaming Multipath TCP (MPTCP) promises significant performance gains by using several network adapters and TCP flows simultaneously for requesting data. This concept could be especially useful for applications requiring high data volumes and low delays such as Dynamic Adaptive Streaming over HTTP (DASH). The task in this seminar is therefore to identify existing research for applying the MPTCP concept for DASH and discuss challenges, potentials as well as current developments in this area. Corbillon, Xavier, et al. "Cross-Layer Scheduler for Video Streaming over MPTCP." Proc. of ACM MMSys (2016) Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 11

12 SDN1: Classification of SDN Forwarding Mechanisms SDN is a hot topic in network research; many papers introduce new forwarding mechanisms. While the approaches differ in their goals and application domains, the basic concepts are often quite similar. Therefore, a thesis is currently in progress at our lab that provides a first classification of SDN forwarding mechanisms. The goal of this topic is to use the available classification as a starting point and expand it to cover more mechanism and improve the classification. As an example, the concepts described in [1], [2], and [3] share a lot of similarities and could be grouped into a single class. [1] E.Rosen, A. Viswanathan, and R. Callon, Multiprotocol Label Switching Architecture, RFC 3031, IETF, 2001 [2] Minlan Yu, Jennifer Rexford, Michael J. Freedman, and Jia Wang. Scalable flow-based networking with DIFANE. ACM SIGCOMM. [3] As Iyer, V Mann, and Nr Samineni. SwitchReduce: Reducing switch state and controller involvement in OpenFlow networks. IFIP Networking Conference, Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 12

13 Any Questions? Department of Computer Science Science Chair: Distributed Multimedia Systems Prof. Effelsberg 13

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