1. Department of Information Technology – Broadband Communication Networks (IBCN) Dynamic QoE Optimisation for Streaming Content in Large- Scale Future Networks Jeroen Famaey, Bart De Vleeschauwer, Tim Wauters, Filip De Turck, Bart Dhoedt, Piet Demeester jeroen.famaey@intec.ugent.be www.ibcn.intec.ugent.be INTEC Broadband Communication Networks (IBCN) Department of Information Technology (INTEC) Ghent University

2. Department of Information Technology – Broadband Communication Networks (IBCN) 2 Overview Introduction Server and Bit-Rate Selection Evaluation Conclusions & Future Work

3. Department of Information Technology – Broadband Communication Networks (IBCN) 3 Introduction – Current Situation Video-based services are highly popular Video sharing Internet-TV … Video quality Usually low to very low No dynamic quality adaptation Centralised delivery architecture Scales poorly Single point-of-failure

4. Department of Information Technology – Broadband Communication Networks (IBCN) 4 Introduction – Our Approach  Content hosting  Content discovery  Quality adaptation  Bwd monitoring  Overlay routing  Content discovery  Content selection  Bwd monitoring

5. Department of Information Technology – Broadband Communication Networks (IBCN) 5 Overview Introduction Server and Bit-Rate Selection Protocol Description Static Selection Algorithm Dynamic Selection Algorithm Evaluation Conclusions & Future Work

6. Department of Information Technology – Broadband Communication Networks (IBCN) 6 Server and Bit-Rate Selection Competing goals Improve end-user Quality of Experience (QoE) Admit as many end-user requests as possible What is Quality of Experience? Jitter Delay Packet loss Bit-rate … Transcoding plugins Bit-rate  QoE Computational resources  QoE End-user perception

7. Department of Information Technology – Broadband Communication Networks (IBCN) 7 Server and Bit-Rate Selection Discovery Selection Accept Reject

8. Department of Information Technology – Broadband Communication Networks (IBCN) 8 Static Selection Algorithm (1) Sorts servers and plugins based on metric Naïve metrics MinRes: minimise computational-resource usage  Maximises bit-rate and QoE  Best under computational-resources bottleneck MinBwd: minimise bandwidth usage  Minimises bit-rate and QoE  Best under bandwidth bottleneck Weighted metric Servers: Plugins:

9. Department of Information Technology – Broadband Communication Networks (IBCN) 9 Static Selection Algorithm (2) α in [0,1] = normalised resource usage β in [0,1] = normalised bandwidth usage αβ action low use high bit-rate plugin lowhighuse low bit-rate plugin highlowuse high bit-rate plugin high use medium bit-rate plugin Requirements: w 1 = 1 + α - βw 2 = β

10. Department of Information Technology – Broadband Communication Networks (IBCN) 10 Dynamic Selection Algorithm Only used for plugin selection Adapt bit-rate while video is playing Admit new stream: For all plugins P i (sorted by decreasing bit- rate)  Downscale all videos that use P i-1 to P i  If: enough resources and bandwidth, use P i Remove finished stream: For all plugins P i (sorted by increasing bit- rate)  Upscale videos that use P i to P i+1

11. Department of Information Technology – Broadband Communication Networks (IBCN) 11 Overview Introduction Server and Bit-Rate Selection Evaluation Simulation Setup Static Scenarios Dynamic Scenarios Conclusions & Future Work

12. Department of Information Technology – Broadband Communication Networks (IBCN) 12 Evaluation – Simulation Setup Implemented in PlanetSim P2P simulator Network 20 content servers 50 client proxies (actual clients not simulated) Transcoding plugins P 1 : 350 Kbps, 1000 Mhz, 80% QoE P 2 : 700 Kbps, 500 Mhz, 90% QoE P 3 : 1400 Kbps, 100 Mhz, 100% QoE Videos 100 different videos Duration in [1,11] minutes interval (YouTube-like use-case) Popularity directly proportional to availability (linear) Popularity directly proportional to request count (zipf) Evaluation metrics Satisfied request count Globally obtained QoE

13. Department of Information Technology – Broadband Communication Networks (IBCN) 13 Evaluation – Static Scenarios Request rate remains constant during simulation Scenarios Computational-resource bottleneck Bandwidth bottleneck Computational-resource and bandwidth bottleneck (shown)

14. Department of Information Technology – Broadband Communication Networks (IBCN) 14 Evaluation – Dynamic Scenarios Scenarios Static (computational-resource and bandwidth bottleneck) Dynamic 1 (increase from 30 to 125 requests per minute) Dynamic 2 (video durations multiplied by 10) (shown)

15. Department of Information Technology – Broadband Communication Networks (IBCN) 15 Overview Introduction Server and Bit-Rate Selection Evaluation Conclusions & Future Work

16. Department of Information Technology – Broadband Communication Networks (IBCN) 16 Conclusions and Future Work Contributions Scalable QoE-aware video delivery architecture Protocol and algorithms to adapt video quality  Maximise obtained QoE  Admit as many client requests as possible Evaluation Intelligent weighted static metric performs up to 30% better than naïve metrics Dynamic algorithm mostly useful in context of longer video durations (e.g. Video-on- Demand) Future work Reduce assumptions made Scalable overlay routing protocols to route around bandwidth bottlenecks in the core network

17. Department of Information Technology – Broadband Communication Networks (IBCN) Questions ? Jeroen Famaey, Bart De Vleeschauwer, Tim Wauters, Filip De Turck, Bart Dhoedt, Piet Demeester jeroen.famaey@intec.ugent.be www.ibcn.intec.ugent.be INTEC Broadband Communication Networks (IBCN) Department of Information Technology (INTEC) Ghent University