Sky Italia - Operation Evolution. London March 20th, 2018

Transcription

1 1 Sky Italia - Operation Evolution London March 20th, 2018

2 Sky Italy to IP-based distribution Content Transmission Contribution Network Core Network Access Network (FTTx) Home Network Content Display Public Internet / Global CDNs Headend Managed CDN Headend Managed CDN Private CDN Headend 2

3 Manage the entropy of IP

4 CDN Selector BI CRM Daily Logs Realtime Macro Alerts Conviva Tech Reporting Off-line CDN Logs Telemetry Intelligence Real time Logs Tracking Beacon 1 minutes hearbeat 4 catalaogues made with 3 streaming protocols and 3 DRMS Original Manifest CDN SELECTOR Distributed over 2 datacenters and 2 public clouds Personalized Manipulated Manifest Encoding superset of bitrates for linear, VOD and D&P Origin Server Content Chunks CDNs Content Chunks Client devices 7 CDNs: ISP local, global and private 4

5 Define a QoS

6 vqos and Sky Service Priority Provide high quality experience during multiple event of live-vod streaming + 1 or more concurrent streaming events. Lower priority services are limited via: Simulation of network QoS experience Throttling of stream delivery on edge server Midstream switching between same service/different capped bandwidth FQDN, based on telemetry information Collection of real time concurrent activity via telemetria Priority list: VOD/Linear streaming, OTT streaming, Recording/D&P 6

7 7 vqos and Sky Service Priority

8 Improve the CDN distribution capabilities

9 New CDN Ranking and Allocation Logic

10 High-Level Functioning of the Algorithm The current prototype algorithm routes a given IP aggregation at given time following 3 main steps. Input variables to the algorithm can be weighed as wished Bitrate Number of errors Rebuffering events & time Quality of Service Index QoS-driven CDN ranking QoS-driven allocation The output of the algorithm for a given IP aggregation (in this case IP /20) and time instance is the following table reporting the fundamental results of the calculations. 10 CDN # SSTT Events QoS Index Final Allocation CDN % CDN % CDN % CDN % CDN % CDN 6 0 NA 8.30%

11 Cloud Scalability

12 Video Encoding Workflow as it was Standard onpremise VOD solution VOD (on STB, Box sets, Kid Apps, Sky Go, Nowtv) Transcoding Multiplexing Packaging Encryption Distribution CDNs Content Ingest Content Master File 50Mb/s High Resolution/ MPEG2 Persistent File 8-15Mb/s 1080i H264 Mb/s Persistent Clear Cache Multi bitrate mp4 1080p up to 5Mb/s Temporary Clear Packaged Cache HLS, HSS, Dash, TS 1080p up to 5Mb/s OTT Catalogue HLS, HSS, Dash, TS 1080p up to 5Mb/s 12

13 Video Encoding Workflow Evolution Standard cloud VOD solution Standard onpremise VOD solution Content Ingest Content Master File 50Mb/s High Resolution/ MPEG2 Transcodin g Transcodin g Multiplexin g Cloud platforms Sky Technology DC Amazon Web Service Google Cloud Engine Persistent Clear Cache Multi bitrate mp4 1080p up to 5Mb/s Packaging VOD (on STB, Box sets, Kid Apps, Sky Go, NowTV) Encryption Temporary Clear OTT Catalogue Packaged Cache HLS, HSS, Dash, TS HLS, HSS, Dash, TS 1080p up to 5Mb/s 1080p up to 5Mb/s Distribution CDNs 13

14 Video Encoding Workflow: Sky Italia Solution Sky Italia VOD Hybrid solution can reduce the lead time for delivering VOD assets while keeping high signal quality without increasing costs 1st Step Transcodin g 2nd Step Transcodin g Cloud platforms Red Hat Openshift Public Cloud Providers VOD (on STB, Box sets, Kid Apps, Sky Go, Nowtv) Multiplexing Packaging Encryption Distribution Content Ingest Content Master File 50Mb/s High Resolution/ MPEG2 Persistent File 8-15Mb/s 1080i H264 Mb/s Persistent Clear Cache Multi bitrate mp4 1080p up to 5Mb/s Temporary Clear Packaged Cache HLS, HSS, Dash, TS 1080p up to 5Mb/s OTT Catalogue HLS, HSS, Dash, TS 1080p up to 5Mb/s 14

15 Low Latency

16 Low Latency A self developed origin server threats the streaming signal to be served with a minimal delay to live It works with any standard players Processing: 2 chunks look-ahead manifest 2s chunks Serve-before-close file delivery All clients aligned to the same chunk More aggressive processing and latency could be reached with dedicated players /tg24/index.m3u8?align =always 16

17 Video Artificial Intelligent

18 Binge Watching To detect the opening and closing credits in a video and also discriminates non interesting credits from storytelling credits which are slightly different from episode to episode and are considered part of the show by viewers. Next Episode in 5 S1: E 12 Two Swords PLAY Episode 11 Episode Thanks to video analytics is possible to extract more

19 Video Content Recognition FACE RECOGNITION Artificial Intelligence technology can be used to add celebrity tagging and face detection capabilities to existing systems, in order to let the automatic recognition of celebrities during the playback of usual video contents of the distribution pipeline. NUDE DETECTION AI technology can be applied to analyze Sky videoclips (or coming from 3 rd parties), to automatically detect those with inappropriate content containing nude scenes, to be discarded / censored. METADATA EXTRACTION Applying objects recognition functionalities to videos, it s possible to detect and tag particular types of scenes based on specific objects they may contain (e.g., «violent» sequences may be detected in a movie, when «shoots» or «blood» are recognized in those scenes) VIDEO CONTENT RECOGNITION APPLICATIONS VIDEO ANALYSIS Algorithms may be defined and used to train AI systems starting from human / subjective evaluations and categorizations, aiming to assess how much a video can be classified as violent, or comic, or belonging to other categories. 19

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21 Quality Optimization Optimization Objective: maximizing the perceptual quality, maintaining the classical constraints of CBR encoding Constan t Bitrate Approac h Constan t Quality Approac h Quality Objective: optimizing the quality/bitrate ratio. Providing a controlled quality in contentaware VBR to reduce costs and optimize QoE 21

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