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Multi-Criteria Optimization of Content Delivery within the Future Media Internet

This document summarizes Joachim Bruneau-Queyreix's PhD defense. The defense addressed multi-criteria optimization of content delivery for the future media internet. The document provides background on challenges facing content delivery such as increasing traffic demands and need for improved quality of experience. It also summarizes several proposed solutions explored in the PhD research, including MS-Stream which relies on simultaneous usage of multiple servers, MATHIAS which exploits multiple distributed resources for each client, and a hybrid P2P/CDN solution. The defense evaluated these approaches and their ability to improve reliability, scalability and quality of experience for adaptive streaming over HTTP.

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Multi-Criteria Optimization of Content Delivery for the Future Media Internet Ph.D Jury: - Daniel NÉGRU (Academic Advisor) - Baptiste DE MEERLER (Industrial Advisor) - Hermann HELLWAGNER (Reviewer) - François TAIANI (Reviewer) - Jordi MONGAY BATTALA (Examiner) - Laurent RÉVEILLÈRE (Examiner) - Christian TIMMERER (Examiner) - Yiping CHEN (Examiner) Defended on : 21-11-2017 Joachim BRUNEAU-QUEYREIX
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 2 Future Media Internet: Context aware Content aware • Network status • Device capabilities • Available resources • Content consumption • Live Content • VoD Content • User Generated Content • Live Conferencing • Interactive Content Today’s Media Internet: • More users every day • Demand for better Quality of Experience (QoE) in constant increase • More devices and terminals • Enormous demand for content consumption  Network congestion  Scalability issues Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives 0 15 30 45 60 75 90 105 120 2015 2016 2017 2018 2019 2020 Traffic(ExaBytes/month) Video File Sharing Web, email Network infrastructures cannot follow-up with the ever increasing users’ demand Consumer Internet traffic forecast (2015-2020) - Cisco VNI
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 3 End-Users Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Content Providers Network Providers Content Delivery Technological Enablers Over-the-Top (OTT) Content Delivery Value Chain
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 4 Content Delivery Networks Peer-to-peer • High reliability • High deployment and maintenance cost • Self-scaling properties • Lack of control, Unreliable Ex: Akamai, Amazon CloudFront, CloudFlare Ex: Strem.io, PPLive Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Origin Server OTT Media delivery solutions: CDN and P2P Cache server Cache server Cache serverCache server Cache server
Hybrid P2P/CDN infrastructure Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 5 Origin Server Cache Server Cache Server Cache Server Cache Server Cache Server Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives High reliability and reduced scalability costs Ex: Xunlei Kankan [Zhang et al 2015] Zhang, G., Liu, W., Hei, X. and Cheng, W., 2015. Unreeling Xunlei Kankan: understanding hybrid CDN-P2P video-on-demand streaming. IEEE Transactions on Multi- media, 17(2):229–242.
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 6 2010: DASH 1993: RTP, RTCP, RTSP 1990: HTTP Download HTTP Progressive Download 1996: RTMP 2006: HTTP adaptive bitrate streaming by Move Networks Several proprietary HTTP Adaptive Streaming (HAS) solutions*TCP-based protocol *UDP-based protocol Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Video streaming techniques – A bit of history
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 7 Content Server time Bitrate(quality) Bandwidth time time DownloadedBitrate • Avoids video freezing events • Adapts content quality to network status • Enables low deployment cost and smooth integration into existing infra (CDNs, Clouds, etc.) Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives DASH: DynamicAdaptive Streaming over HTTP
High reliability Over-The-Top streaming solutions (CDNs, Cloud) Improved QoE potential HAS-based streaming solutions Ph.D Defence - 21/11/2017 8 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Joachim Bruneau-Queyreix
High reliability Over-The-Top streaming solutions (CDNs, Cloud) Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Ph.D Defence - 21/11/2017 9 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives [Adhikari et al, 2012] Adhikari, V. K. et al., 2012. Unreeling netflix: Understanding and improving multi-CDN delivery. IEEE INFOCOM Joachim Bruneau-Queyreix
High reliability Over-The-Top streaming solutions (CDNs, Cloud) Reduced scalability costs P2P solutions Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Ph.D Defence - 21/11/2017 10 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Joachim Bruneau-Queyreix
High reliability Over-The-Top streaming solutions (CDNs, Cloud) Reduced scalability costs P2P solutions Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Hybrid P2P/CDN solution Ph.D Defence - 21/11/2017 11 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives [Zhang et al, 2015] Zhang, G., Liu, W., Hei, X. and Cheng, W., 2015. Unreeling Xunlei Kankan: understanding hybrid CDN-P2P video-on-demand streaming. IEEE Transactions on Multi- media, 17(2):229–242. Joachim Bruneau-Queyreix
High reliability Over-The-Top streaming solutions (CDNs, Cloud) Reduced scalability costs P2P solutions Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Adaptive P2P streaming solutions Hybrid P2P/CDN solution Ph.D Defence - 21/11/2017 12 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives [Jurca et al, 2007] [Merani et al, 2016] - Merani, M. and Natali, L., 2016. Adaptive streaming in P2P live video systems: A distributed rate control approach. ACM Transactions on Multimedia Computing, Com- munications, and Applications (TOMM), 12(3):46 - Jurca, D. and Frossard, P., 2007. Video Packet Selection and Scheduling for Multipath Streaming. IEEE Transactions on Multimedia, 9(3):629–641. Joachim Bruneau-Queyreix
High reliability Over-The-Top streaming solutions (CDNs, Cloud) Reduced scalability costs P2P solutions Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Adaptive P2P streaming solutions Hybrid P2P/CDN solution MS-Stream (+MATHIAS) PMS Ph.D Defence - 21/11/2017 13 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives MS-Stream: Relying on the utilization of multiple servers simultaneously MATHIAS: Exploiting multiple distributed resources for each client PMS: More efficient utilization of resources Joachim Bruneau-Queyreix
MS-Stream: Multiple-Source Streaming over HTTP Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 14 A pragmatic evolving streaming solution targeting QoE increase by relying on the simultaneous usage of multiple servers Related publications: [IEEE ICC2016], [IEEE Multimedia2017], [IEEE CCNC2017]
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 15 Content Server time Bitrate(quality) Bandwidth time time Bitrate Server overload ? Network path overload  User’s “rage” You need more than one server! Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives DASH: DynamicAdaptive Streaming over HTTP • Avoids video freezing events • Adapts content quality to network status • Enables low deployment cost and smooth integration into existing infra (CDNs, Clouds, etc.)
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 16 Content Delivery (simultaneous use of multiple servers) 1200 Kbps Up to a 2000kbps visual quality Content Delivery (One server to one client) 1200 Kbps Up to a 1200kbps visual quality Content Provider Content Ingestion Content Replication CDN Provider Resource environment Control and Management Content Replication Resource environment Control and Management Content Provider Content Ingestion Resource Provider (Clouds, Set-Top-Boxes, CDNs, ISPs) The proposed multiple-source streaming over HTTP approach Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Any content source participates to the streaming up to its upload capacity Uni-source adaptive streaming solution Multi-source adaptive streaming solution
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 17 2 Mbps Up to a 4 Mbps quality Sub-segment delivery3 2Sub-segment composition 4 Sub-segment aggregation + Display 5 Adaptation Multiple-Source Streaming over HTTP (MS-Stream) (i) The obtained visual quality increases as servers join the streaming session (ii) If any server leaves the session, the video playback continuity is insured Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
18 Multiple Description Coding • Classic Encoding: Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix MDC Encoder Output File 1 (i.e. Description #1 OR sub-segment #1) Output File 2 (i.e. Description #2 OR sub-segment #2) .YUV Video Encoder Input Video File (90GB) Output File (900MB, degraded yet acceptable quality) .YUV .h264 Encoding info (Bitrate, QP param, entropy coder) • Multiple Description Encoding: *M. Kazemi, S. Shirmohammadi, and K. H. Sadeghi, “A review of multiple description coding techniques for error-resilient video delivery,” Multimedia Systems, vol. 20, no. 3, pp. 283–309, 2013. Standard-compliant? Low complexity? Redundancy/Data overhead tunability? High possible number of descriptions? Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
19 Video coding Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix I P ... I B B PP Group Of Pictures #1 GoPs #2 ...P P GOP #1GOP GOP GOP GOP GoP Video stream One video segment Time GOP GOP GOP GoP ... Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 20 • Pre-decoding sub-segment aggregation Any Standard Decoder Sub-Segment Aggregation Reconstructed video sequence Output File OUTPUT.YUV s1 s3 s2 • Post-encoding sub-segment composition The proposed Multiple Description Coding scheme Sub-segment composer at MS-Stream server Content qualities available at MS-Stream server Time Time GOP #1GOP GOP GOP GOP GoPs GOP #1GOP GOP GOP GOP GoPs... Sub-segment 2 GOPGOP GOP GOP GOP GOPs Sub-segment 3 GOP #1GOP GOP GOP GOP GOPs Sub-segment 1 GOP GOP GOP GOP GOP GoPs ✔ Video codec standard compliant ✔ Low complexity ✔ Redundancy/Data overhead tunability ✔ High possible number of descriptions High bitrate GoPsGoPs Redundant bitrate GoPs GoPs Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
DASH client/server architecture Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 21 b Kbps HTTP Server DASH Storage HTTP Client Standard Decoder Adaptation Engine DASH Client b Kbps Segment request DASH Server Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
22Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix MS-Stream system Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives MS-Stream Servers HTTP Client Sub-segment aggregator Standard Decoder Adaptation Engine MS-Stream Client a kbps b kbps c kbps MS-Stream HTTP API DASH Storage Sub-segment composer MS-Stream HTTP API DASH Storage Sub-segment composer MS-Stream HTTP API DASH Storage Sub- segment composer
Evolution • A multiple-source adaptive streaming solution over HTTP • Potential to increase QoE for end-users Complexity of available resources • How much bandwidth consumption overhead (from redundant GoPs)? • Optimal number of used servers? • Adaptation to heterogeneous paths/servers’ capacities? • Late sub-segment deliveries? Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 23 Content Server Content Server Content Server ? ... ... Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
MATHIAS: Multiple-source and Adaptive sTreamIng AlgorithmS Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 24 A set of heuristics to tackle multiple-source adaptive streaming content consumption Related publications: [IEEE ICME2017], [IEEE CCNC2017-demo]
MATHIAS: a two-phase adaptation algorithm (client-side) Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 25 Merge and display Sub-segment scheduling & Adaptation to path heterogeneity Prior-download adaptation decisions In-download adaptation In-segment download adaptation Server #1 Server #... Server #M (optional) Inputs for next segment download: buffer level, server throughput estimations Overhead selection + Bitrate adaptation Selected content bitrate Manifest file Objectives: • A target visual content quality at Y Mbps • A maximum percentage of bandwidth consumption overhead Omax Bandwidth bottleneck estimation + Server adaptation List of servers to be used Throughput estimation Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Remaining issues: 1) How much bandwidth consumption overhead (from redundant GoPs)? 2) Optimal number of used servers? 3) Adaptation to heterogeneous paths/servers’ capacity? 4) Late requests?
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 26 Percentageof Overhead(%) Buffer Occupancy Level (sec) ε σ Omin Omax 0 Objective: A maximum percentage of bandwidth consumption overhead Omax Dynamically adjusting the overhead for each segment Buffer-based bandwidth overhead adaptation (1/4) GOP #1 GOP GOP GOP GOP GoPs Null bitrate GoPsGoPsHigh bitrate GoPsGoPs Redundant and low bitrate GoPsGoPs Sub-segment composer at MS-Stream server Content qualities available at MS- Stream server Time Example of possible sub- segment including GoPs with no payload Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives A more flexible sub-segment composition Redundant GoPs necessary ?
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 27 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Objective: Target visual content quality at Y Mbps Two steps: 1. Determining bandwidth bottleneck location (client-side, server-side) 2. Adjusting the number of considered servers Adaptation of the number of used servers (2/4) Additive-Increase Multiplicative- Decrease (AIMD) server adaptation Download two video segments Bandwidth bottleneck location estimation Monitor impact of server adaptation on global throughput and per-server throughput Limited throughput? Bottleneck at server-side  Increase number of servers Bottleneck at client-side  Halve the number of servers
Adaptation to paths’ capacity heterogeneity (3/4) Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 28 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives How many GoPs from each server ?  Use of the considered servers in proportion to their observed throughput capacities ?? ?
Feedback control loop for in-segment download adaptation (4/4) Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 29 Download next segment Downloads finished? In-segment download adaptation? Perform adaptation rules SleepTrue False False True Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Rules’ objectives: maintain the buffer occupancy level in its zone - Terminate late requests and prevent video stalls - Hand the estimated late requests over the best performing server Percentageof bandwidthoverhead Buffer Occupancy Level bufLeveln (sec) ε σ Omin Omax 0 Late sub-segment deliveries?
Evaluations Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 30
0 2 4 6 8 10 12 14 16 18 0 5 10 15 20 25 30 35 40 45 50 55 Mbps Segment Index aggregated thoughput video bitrate redundant data 0 2 4 6 8 10 12 14 16 18 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 Trhoughput(Mbps) Segment Index Server 7 Server 9 Server 8 Server 10 Server 6 Server 5 Server 3 Server 4 Server 2 Server 1 Server bottleneck Client bottleneck Unknown bottleneck Server bottleneck Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 31 Throughput limitation at server-side Throughput limitation at client-side Testbed: • 10 servers • 2.5 Mbps per server • One client (implemented in the dash.js player from DASH-IF*) • Controlled environment *http://dashif.org 0 2 4 6 8 10 12 14 16 18 20 22 24 0 10 20 30 40 50 60 0 50 100 150 200 250 300 Percentageofoverhead Bufferlevelinseconds 5me (sec) Buffer level ε σ overhead rule (ii) rule (iii) rule (ii) rule (i) %
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 32 MS-Stream (+MATHIAS) QoE evaluation • 10 available servers in labs/end-users’ homes in France, Poland, Greece, Romania • 8 available qualities (1,2,3,4,5,6,7,8Mbps) • A target quality at 8 Mbps • Redundant bitrate at 200kbps • Bandwidth overhead set to 10% maximum • A 10-min video • 50 runs per experiment (100 hours of streaming) Evaluated streaming application Global available throughput Number of clients Number of available servers Throughput per server DASH 20 Mbps 2 1 20 Mbps MS-Stream (+MATHIAS) 20 Mbps 2 10 2 Mbps Evaluated streaming application Mean Bitrate (Mbps) Quality changes Video freezing events Start-up delay (sec) Bandwidth overhead Average used servers DASH 6.87 10.32 7.22 4.11 0% 1 MS-Stream (+MATHIAS) 7.78 4.69 0.29 1.56 6.43% 7.68 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
PMS: Quality and scale adaptive hybrid P2P/Multi-Server solution for live- streaming Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 33 A pragmatic evolving streaming solution targeting QoE increase by relying on the simultaneous usage of multiple servers Related publications: [ICME/DASH-IF Grand-challenge2017], [ACM MM2017-demo], [ACM TOMM(submitted)]
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 34 Content servers Data flow Control flow Management Services Up to a 4 Mbps quality 2 Mbps P2P overlays #Qmax #i #1 Trackers The proposed multi-overlay hybrid P2P/Multi-Server (PMS) architecture Each overlay #i is composed of peers currently consuming and re-emitting the quality #i only Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Trackers evaluate the “health” of the system and of each overlay with several global indicators Peers report local system metrics (throughput, received data, sent data) to the trackers
Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 35 Inputs for segment n=n+1 (buffer level, throughput estimation, etc...) PMS quality adaptation PMS scale adaptation Sub-segment scheduling P2P in- segment adaptation Peer #1 Peer #... Peer #K X% of data from servers at selected bitrate bi 1-X% of data from peers at bitrate bi Tracker and P2P communications Merge and display Sub-segment scheduling Overhead selection Bottleneck estimation + Server Adaptation In-segment download adaptation Server #1 Server #... Server #M (optional)List of servers to be used Selected content bitrate MATHIAS Manifest File Decision flow Control flow Content requests Content delivery Multi-ServerMulti-Peer (optional) Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives The proposed PMS consumption algorithm Selected bitrate bi In-download adaptationPrior-download adaptation decisions
PMS distributed quality adaptation Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 36 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Retrieve global indicators Compute local indicators Overlay migration decision Overlay capacity Quality delivery efficiency Content servers Which overlay? P2P overlays #Qmax #i #1 Trackers ? ? ? Throughput from servers Throughput from peers Migration decision for next segment download in order to preserve the good functioning of all overlays Next video bitrate
PMS distributed scale adaptation Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 37 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Probabilistic approach for GoPs delivery assignation to peers  lowering P2P communications for real-time constraint applications Retrieve global indicators Adjusting percentage of GoPs requested to the servers Global P2P efficiency Compute local indicators Local P2P capacity • X% of GoPs to be requested from the servers • 1-X% from the peers Slowly moving towards an increase of the peers’ capacity utilization without ceding on the QoE gains Content servers Servers? Peers ? Both ? P2P overlays #Qmax #i #1 Trackers How many GoPs ? How many GoPs ? Probability to assigned the delivery of GoPs c to peer k:
Evaluations Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 38
Evaluations • 15 servers with 25 Mbps upload throughput capacity • 2 applications (PMS and 1 re-implemented research paper) • 1, 2, 4, 6 Mbps content quality • 300 peers (Paris, Roubaix, Versailles, Bordeaux) • 150 peers for the first 15 minutes, 150 more peers at the 16th minute Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 39 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Application Consumption and adaptation features PMS-AQAS MS-Stream + MATHIAS + Adaptive-Quality + Adaptive-Scale P2P-DASH [Merani et al, 2016] The server is used at 4 times the rate of the consumed video qualities for the entire streaming system Evaluated parameters • Rebuffering events • Quality changes • Mean bitrate • Percentage data coming from the servers Merani, M. and Natali, L., 2016. Adaptive streaming in P2P live video systems: A distributed rate control approach. ACM Transactions on Multimedia Computing, Com- munications, and Applications (TOMM), 12(3):46
Results Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 40 Application Mean bitrate Rebuffering events (per minute) Quality changes (per minute) % of data from servers % of data from P2P PMS-AQAS ~6Mbps <0.1 <0.3 ~45% ~55% P2P-DASH 2.55 ~2.5 2.15 <1% >99% Application Mean bitrate Rebuffering events (per minute) Quality changes (per minute) % of data from servers % of data from P2P PMS-AQAS ~6Mbps <0.1 <0.3 ~24% ~76% P2P-DASH 4.1 Mbps ~2.3 2.19 <1% >99% Results before flash crowd (per-client results) Results after flash crowd (per-client results) Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
Conclusion and perspectives Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 41 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives MS-Stream MATHIAS PMS IoT streaming Social Networks of Objects Omnidirectional video streaming Framework for multiple-server HAS QoE and scalability increase QoE increase
Thank you for your attention! Time for a Q&A session Demo available at http://msstream.net Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 42

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Multi-Criteria Optimization of Content Delivery within the Future Media Internet

  • 1.
    Multi-Criteria Optimization ofContent Delivery for the Future Media Internet Ph.D Jury: - Daniel NÉGRU (Academic Advisor) - Baptiste DE MEERLER (Industrial Advisor) - Hermann HELLWAGNER (Reviewer) - François TAIANI (Reviewer) - Jordi MONGAY BATTALA (Examiner) - Laurent RÉVEILLÈRE (Examiner) - Christian TIMMERER (Examiner) - Yiping CHEN (Examiner) Defended on : 21-11-2017 Joachim BRUNEAU-QUEYREIX
  • 2.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 2 Future Media Internet: Context aware Content aware • Network status • Device capabilities • Available resources • Content consumption • Live Content • VoD Content • User Generated Content • Live Conferencing • Interactive Content Today’s Media Internet: • More users every day • Demand for better Quality of Experience (QoE) in constant increase • More devices and terminals • Enormous demand for content consumption  Network congestion  Scalability issues Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives 0 15 30 45 60 75 90 105 120 2015 2016 2017 2018 2019 2020 Traffic(ExaBytes/month) Video File Sharing Web, email Network infrastructures cannot follow-up with the ever increasing users’ demand Consumer Internet traffic forecast (2015-2020) - Cisco VNI
  • 3.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 3 End-Users Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Content Providers Network Providers Content Delivery Technological Enablers Over-the-Top (OTT) Content Delivery Value Chain
  • 4.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 4 Content Delivery Networks Peer-to-peer • High reliability • High deployment and maintenance cost • Self-scaling properties • Lack of control, Unreliable Ex: Akamai, Amazon CloudFront, CloudFlare Ex: Strem.io, PPLive Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Origin Server OTT Media delivery solutions: CDN and P2P Cache server Cache server Cache serverCache server Cache server
  • 5.
    Hybrid P2P/CDN infrastructure Ph.DDefence - 21/11/2017 Joachim Bruneau-Queyreix 5 Origin Server Cache Server Cache Server Cache Server Cache Server Cache Server Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives High reliability and reduced scalability costs Ex: Xunlei Kankan [Zhang et al 2015] Zhang, G., Liu, W., Hei, X. and Cheng, W., 2015. Unreeling Xunlei Kankan: understanding hybrid CDN-P2P video-on-demand streaming. IEEE Transactions on Multi- media, 17(2):229–242.
  • 6.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 6 2010: DASH 1993: RTP, RTCP, RTSP 1990: HTTP Download HTTP Progressive Download 1996: RTMP 2006: HTTP adaptive bitrate streaming by Move Networks Several proprietary HTTP Adaptive Streaming (HAS) solutions*TCP-based protocol *UDP-based protocol Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Video streaming techniques – A bit of history
  • 7.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 7 Content Server time Bitrate(quality) Bandwidth time time DownloadedBitrate • Avoids video freezing events • Adapts content quality to network status • Enables low deployment cost and smooth integration into existing infra (CDNs, Clouds, etc.) Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives DASH: DynamicAdaptive Streaming over HTTP
  • 8.
    High reliability Over-The-Top streaming solutions(CDNs, Cloud) Improved QoE potential HAS-based streaming solutions Ph.D Defence - 21/11/2017 8 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Joachim Bruneau-Queyreix
  • 9.
    High reliability Over-The-Top streaming solutions(CDNs, Cloud) Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Ph.D Defence - 21/11/2017 9 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives [Adhikari et al, 2012] Adhikari, V. K. et al., 2012. Unreeling netflix: Understanding and improving multi-CDN delivery. IEEE INFOCOM Joachim Bruneau-Queyreix
  • 10.
    High reliability Over-The-Top streaming solutions(CDNs, Cloud) Reduced scalability costs P2P solutions Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Ph.D Defence - 21/11/2017 10 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Joachim Bruneau-Queyreix
  • 11.
    High reliability Over-The-Top streaming solutions(CDNs, Cloud) Reduced scalability costs P2P solutions Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Hybrid P2P/CDN solution Ph.D Defence - 21/11/2017 11 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives [Zhang et al, 2015] Zhang, G., Liu, W., Hei, X. and Cheng, W., 2015. Unreeling Xunlei Kankan: understanding hybrid CDN-P2P video-on-demand streaming. IEEE Transactions on Multi- media, 17(2):229–242. Joachim Bruneau-Queyreix
  • 12.
    High reliability Over-The-Top streaming solutions(CDNs, Cloud) Reduced scalability costs P2P solutions Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Adaptive P2P streaming solutions Hybrid P2P/CDN solution Ph.D Defence - 21/11/2017 12 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives [Jurca et al, 2007] [Merani et al, 2016] - Merani, M. and Natali, L., 2016. Adaptive streaming in P2P live video systems: A distributed rate control approach. ACM Transactions on Multimedia Computing, Com- munications, and Applications (TOMM), 12(3):46 - Jurca, D. and Frossard, P., 2007. Video Packet Selection and Scheduling for Multipath Streaming. IEEE Transactions on Multimedia, 9(3):629–641. Joachim Bruneau-Queyreix
  • 13.
    High reliability Over-The-Top streaming solutions(CDNs, Cloud) Reduced scalability costs P2P solutions Improved QoE potential HAS-based streaming solutions CDN and HAS based streaming Adaptive P2P streaming solutions Hybrid P2P/CDN solution MS-Stream (+MATHIAS) PMS Ph.D Defence - 21/11/2017 13 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives MS-Stream: Relying on the utilization of multiple servers simultaneously MATHIAS: Exploiting multiple distributed resources for each client PMS: More efficient utilization of resources Joachim Bruneau-Queyreix
  • 14.
    MS-Stream: Multiple-Source Streaming overHTTP Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 14 A pragmatic evolving streaming solution targeting QoE increase by relying on the simultaneous usage of multiple servers Related publications: [IEEE ICC2016], [IEEE Multimedia2017], [IEEE CCNC2017]
  • 15.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 15 Content Server time Bitrate(quality) Bandwidth time time Bitrate Server overload ? Network path overload  User’s “rage” You need more than one server! Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives DASH: DynamicAdaptive Streaming over HTTP • Avoids video freezing events • Adapts content quality to network status • Enables low deployment cost and smooth integration into existing infra (CDNs, Clouds, etc.)
  • 16.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 16 Content Delivery (simultaneous use of multiple servers) 1200 Kbps Up to a 2000kbps visual quality Content Delivery (One server to one client) 1200 Kbps Up to a 1200kbps visual quality Content Provider Content Ingestion Content Replication CDN Provider Resource environment Control and Management Content Replication Resource environment Control and Management Content Provider Content Ingestion Resource Provider (Clouds, Set-Top-Boxes, CDNs, ISPs) The proposed multiple-source streaming over HTTP approach Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Any content source participates to the streaming up to its upload capacity Uni-source adaptive streaming solution Multi-source adaptive streaming solution
  • 17.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 17 2 Mbps Up to a 4 Mbps quality Sub-segment delivery3 2Sub-segment composition 4 Sub-segment aggregation + Display 5 Adaptation Multiple-Source Streaming over HTTP (MS-Stream) (i) The obtained visual quality increases as servers join the streaming session (ii) If any server leaves the session, the video playback continuity is insured Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
  • 18.
    18 Multiple Description Coding •Classic Encoding: Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix MDC Encoder Output File 1 (i.e. Description #1 OR sub-segment #1) Output File 2 (i.e. Description #2 OR sub-segment #2) .YUV Video Encoder Input Video File (90GB) Output File (900MB, degraded yet acceptable quality) .YUV .h264 Encoding info (Bitrate, QP param, entropy coder) • Multiple Description Encoding: *M. Kazemi, S. Shirmohammadi, and K. H. Sadeghi, “A review of multiple description coding techniques for error-resilient video delivery,” Multimedia Systems, vol. 20, no. 3, pp. 283–309, 2013. Standard-compliant? Low complexity? Redundancy/Data overhead tunability? High possible number of descriptions? Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
  • 19.
    19 Video coding Ph.D Defence- 21/11/2017 Joachim Bruneau-Queyreix I P ... I B B PP Group Of Pictures #1 GoPs #2 ...P P GOP #1GOP GOP GOP GOP GoP Video stream One video segment Time GOP GOP GOP GoP ... Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
  • 20.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 20 • Pre-decoding sub-segment aggregation Any Standard Decoder Sub-Segment Aggregation Reconstructed video sequence Output File OUTPUT.YUV s1 s3 s2 • Post-encoding sub-segment composition The proposed Multiple Description Coding scheme Sub-segment composer at MS-Stream server Content qualities available at MS-Stream server Time Time GOP #1GOP GOP GOP GOP GoPs GOP #1GOP GOP GOP GOP GoPs... Sub-segment 2 GOPGOP GOP GOP GOP GOPs Sub-segment 3 GOP #1GOP GOP GOP GOP GOPs Sub-segment 1 GOP GOP GOP GOP GOP GoPs ✔ Video codec standard compliant ✔ Low complexity ✔ Redundancy/Data overhead tunability ✔ High possible number of descriptions High bitrate GoPsGoPs Redundant bitrate GoPs GoPs Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
  • 21.
    DASH client/server architecture Ph.DDefence - 21/11/2017 Joachim Bruneau-Queyreix 21 b Kbps HTTP Server DASH Storage HTTP Client Standard Decoder Adaptation Engine DASH Client b Kbps Segment request DASH Server Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
  • 22.
    22Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix MS-Stream system Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives MS-Stream Servers HTTP Client Sub-segment aggregator Standard Decoder Adaptation Engine MS-Stream Client a kbps b kbps c kbps MS-Stream HTTP API DASH Storage Sub-segment composer MS-Stream HTTP API DASH Storage Sub-segment composer MS-Stream HTTP API DASH Storage Sub- segment composer
  • 23.
    Evolution • A multiple-sourceadaptive streaming solution over HTTP • Potential to increase QoE for end-users Complexity of available resources • How much bandwidth consumption overhead (from redundant GoPs)? • Optimal number of used servers? • Adaptation to heterogeneous paths/servers’ capacities? • Late sub-segment deliveries? Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 23 Content Server Content Server Content Server ? ... ... Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
  • 24.
    MATHIAS: Multiple-source andAdaptive sTreamIng AlgorithmS Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 24 A set of heuristics to tackle multiple-source adaptive streaming content consumption Related publications: [IEEE ICME2017], [IEEE CCNC2017-demo]
  • 25.
    MATHIAS: a two-phaseadaptation algorithm (client-side) Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 25 Merge and display Sub-segment scheduling & Adaptation to path heterogeneity Prior-download adaptation decisions In-download adaptation In-segment download adaptation Server #1 Server #... Server #M (optional) Inputs for next segment download: buffer level, server throughput estimations Overhead selection + Bitrate adaptation Selected content bitrate Manifest file Objectives: • A target visual content quality at Y Mbps • A maximum percentage of bandwidth consumption overhead Omax Bandwidth bottleneck estimation + Server adaptation List of servers to be used Throughput estimation Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Remaining issues: 1) How much bandwidth consumption overhead (from redundant GoPs)? 2) Optimal number of used servers? 3) Adaptation to heterogeneous paths/servers’ capacity? 4) Late requests?
  • 26.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 26 Percentageof Overhead(%) Buffer Occupancy Level (sec) ε σ Omin Omax 0 Objective: A maximum percentage of bandwidth consumption overhead Omax Dynamically adjusting the overhead for each segment Buffer-based bandwidth overhead adaptation (1/4) GOP #1 GOP GOP GOP GOP GoPs Null bitrate GoPsGoPsHigh bitrate GoPsGoPs Redundant and low bitrate GoPsGoPs Sub-segment composer at MS-Stream server Content qualities available at MS- Stream server Time Example of possible sub- segment including GoPs with no payload Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives A more flexible sub-segment composition Redundant GoPs necessary ?
  • 27.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 27 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Objective: Target visual content quality at Y Mbps Two steps: 1. Determining bandwidth bottleneck location (client-side, server-side) 2. Adjusting the number of considered servers Adaptation of the number of used servers (2/4) Additive-Increase Multiplicative- Decrease (AIMD) server adaptation Download two video segments Bandwidth bottleneck location estimation Monitor impact of server adaptation on global throughput and per-server throughput Limited throughput? Bottleneck at server-side  Increase number of servers Bottleneck at client-side  Halve the number of servers
  • 28.
    Adaptation to paths’capacity heterogeneity (3/4) Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 28 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives How many GoPs from each server ?  Use of the considered servers in proportion to their observed throughput capacities ?? ?
  • 29.
    Feedback control loopfor in-segment download adaptation (4/4) Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 29 Download next segment Downloads finished? In-segment download adaptation? Perform adaptation rules SleepTrue False False True Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Rules’ objectives: maintain the buffer occupancy level in its zone - Terminate late requests and prevent video stalls - Hand the estimated late requests over the best performing server Percentageof bandwidthoverhead Buffer Occupancy Level bufLeveln (sec) ε σ Omin Omax 0 Late sub-segment deliveries?
  • 30.
    Evaluations Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 30
  • 31.
    0 2 4 6 8 10 12 14 16 18 0 5 1015 20 25 30 35 40 45 50 55 Mbps Segment Index aggregated thoughput video bitrate redundant data 0 2 4 6 8 10 12 14 16 18 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 Trhoughput(Mbps) Segment Index Server 7 Server 9 Server 8 Server 10 Server 6 Server 5 Server 3 Server 4 Server 2 Server 1 Server bottleneck Client bottleneck Unknown bottleneck Server bottleneck Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 31 Throughput limitation at server-side Throughput limitation at client-side Testbed: • 10 servers • 2.5 Mbps per server • One client (implemented in the dash.js player from DASH-IF*) • Controlled environment *http://dashif.org 0 2 4 6 8 10 12 14 16 18 20 22 24 0 10 20 30 40 50 60 0 50 100 150 200 250 300 Percentageofoverhead Bufferlevelinseconds 5me (sec) Buffer level ε σ overhead rule (ii) rule (iii) rule (ii) rule (i) %
  • 32.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 32 MS-Stream (+MATHIAS) QoE evaluation • 10 available servers in labs/end-users’ homes in France, Poland, Greece, Romania • 8 available qualities (1,2,3,4,5,6,7,8Mbps) • A target quality at 8 Mbps • Redundant bitrate at 200kbps • Bandwidth overhead set to 10% maximum • A 10-min video • 50 runs per experiment (100 hours of streaming) Evaluated streaming application Global available throughput Number of clients Number of available servers Throughput per server DASH 20 Mbps 2 1 20 Mbps MS-Stream (+MATHIAS) 20 Mbps 2 10 2 Mbps Evaluated streaming application Mean Bitrate (Mbps) Quality changes Video freezing events Start-up delay (sec) Bandwidth overhead Average used servers DASH 6.87 10.32 7.22 4.11 0% 1 MS-Stream (+MATHIAS) 7.78 4.69 0.29 1.56 6.43% 7.68 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
  • 33.
    PMS: Quality andscale adaptive hybrid P2P/Multi-Server solution for live- streaming Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 33 A pragmatic evolving streaming solution targeting QoE increase by relying on the simultaneous usage of multiple servers Related publications: [ICME/DASH-IF Grand-challenge2017], [ACM MM2017-demo], [ACM TOMM(submitted)]
  • 34.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 34 Content servers Data flow Control flow Management Services Up to a 4 Mbps quality 2 Mbps P2P overlays #Qmax #i #1 Trackers The proposed multi-overlay hybrid P2P/Multi-Server (PMS) architecture Each overlay #i is composed of peers currently consuming and re-emitting the quality #i only Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Trackers evaluate the “health” of the system and of each overlay with several global indicators Peers report local system metrics (throughput, received data, sent data) to the trackers
  • 35.
    Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 35 Inputs for segment n=n+1 (buffer level, throughput estimation, etc...) PMS quality adaptation PMS scale adaptation Sub-segment scheduling P2P in- segment adaptation Peer #1 Peer #... Peer #K X% of data from servers at selected bitrate bi 1-X% of data from peers at bitrate bi Tracker and P2P communications Merge and display Sub-segment scheduling Overhead selection Bottleneck estimation + Server Adaptation In-segment download adaptation Server #1 Server #... Server #M (optional)List of servers to be used Selected content bitrate MATHIAS Manifest File Decision flow Control flow Content requests Content delivery Multi-ServerMulti-Peer (optional) Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives The proposed PMS consumption algorithm Selected bitrate bi In-download adaptationPrior-download adaptation decisions
  • 36.
    PMS distributed qualityadaptation Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 36 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Retrieve global indicators Compute local indicators Overlay migration decision Overlay capacity Quality delivery efficiency Content servers Which overlay? P2P overlays #Qmax #i #1 Trackers ? ? ? Throughput from servers Throughput from peers Migration decision for next segment download in order to preserve the good functioning of all overlays Next video bitrate
  • 37.
    PMS distributed scaleadaptation Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 37 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Probabilistic approach for GoPs delivery assignation to peers  lowering P2P communications for real-time constraint applications Retrieve global indicators Adjusting percentage of GoPs requested to the servers Global P2P efficiency Compute local indicators Local P2P capacity • X% of GoPs to be requested from the servers • 1-X% from the peers Slowly moving towards an increase of the peers’ capacity utilization without ceding on the QoE gains Content servers Servers? Peers ? Both ? P2P overlays #Qmax #i #1 Trackers How many GoPs ? How many GoPs ? Probability to assigned the delivery of GoPs c to peer k:
  • 38.
    Evaluations Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 38
  • 39.
    Evaluations • 15 serverswith 25 Mbps upload throughput capacity • 2 applications (PMS and 1 re-implemented research paper) • 1, 2, 4, 6 Mbps content quality • 300 peers (Paris, Roubaix, Versailles, Bordeaux) • 150 peers for the first 15 minutes, 150 more peers at the 16th minute Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 39 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives Application Consumption and adaptation features PMS-AQAS MS-Stream + MATHIAS + Adaptive-Quality + Adaptive-Scale P2P-DASH [Merani et al, 2016] The server is used at 4 times the rate of the consumed video qualities for the entire streaming system Evaluated parameters • Rebuffering events • Quality changes • Mean bitrate • Percentage data coming from the servers Merani, M. and Natali, L., 2016. Adaptive streaming in P2P live video systems: A distributed rate control approach. ACM Transactions on Multimedia Computing, Com- munications, and Applications (TOMM), 12(3):46
  • 40.
    Results Ph.D Defence -21/11/2017 Joachim Bruneau-Queyreix 40 Application Mean bitrate Rebuffering events (per minute) Quality changes (per minute) % of data from servers % of data from P2P PMS-AQAS ~6Mbps <0.1 <0.3 ~45% ~55% P2P-DASH 2.55 ~2.5 2.15 <1% >99% Application Mean bitrate Rebuffering events (per minute) Quality changes (per minute) % of data from servers % of data from P2P PMS-AQAS ~6Mbps <0.1 <0.3 ~24% ~76% P2P-DASH 4.1 Mbps ~2.3 2.19 <1% >99% Results before flash crowd (per-client results) Results after flash crowd (per-client results) Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives
  • 41.
    Conclusion and perspectives Ph.DDefence - 21/11/2017 Joachim Bruneau-Queyreix 41 Introduction Background Challenges MS-Stream MATHIAS PMS Conclusion & Perspectives MS-Stream MATHIAS PMS IoT streaming Social Networks of Objects Omnidirectional video streaming Framework for multiple-server HAS QoE and scalability increase QoE increase
  • 42.
    Thank you foryour attention! Time for a Q&A session Demo available at http://msstream.net Ph.D Defence - 21/11/2017 Joachim Bruneau-Queyreix 42