Visualizing Cellular Audience for Streaming KPIs

Transcript

1. © AKAMAI - EDGE 2017 Visualizing Cellular Audience for Streaming KPI’s Ritesh R K, Solutions Engineer
2. © AKAMAI - EDGE 2017 Agenda • Mobile audience trend. • QOS with Akamai Media Analytics. • Network Analytics for Cellular audience. • Finish line and takeaways.
3. © AKAMAI - EDGE 2017 Market Dynamics More than 75% of the viewers stream from mobile networks Per a recently released App Annie report3, Android users in India spent close to 150 billion hours on apps in 2016, leading app usage in the world.
4. © AKAMAI - EDGE 2017 Disrupting Numbers and User Expectations The viewership peaked at more than 3 million concurrent on Akamai establishing a new high in the Asia Pacific region1. More than 25 million users watched the event on the platform.
5. © AKAMAI - EDGE 2017 Quantifying quality with Media Analytics
6. © AKAMAI - EDGE 2017 Visualization today QOS Dashboard with Media Analytics
7. © AKAMAI - EDGE 2017 Visualization today - ASN Data by ASN from Media Analytics
8. © AKAMAI - EDGE 2017 Visualization today-Platform and City Data by City and Platform from Media Analytics
9. © AKAMAI - EDGE 2017 Visualization today-Startup time by City and Platform Data by City and Platform from Media Analytics
10. © AKAMAI - EDGE 2017 All the demographic data is derived from the IP
11. © AKAMAI - EDGE 2017 Avoid data theft and downtime by extending the security perimeter outside the data-center and protect from increasing frequency, scale and sophistication of web attacks. Structure of Network Operators Mobile Network Akamai Edge Server Akamai Edge Server Carrier Grade NAT/ Firewall ISP PoP or IX Mobile Network Public Internet
12. © AKAMAI - EDGE 2017 Under the hood- Radio Access Control is Complicated!
13. © AKAMAI - EDGE 2017 • GEO-IP databases are • inaccurate and need • periodic updates. • Use of proxies and • peering relationships • can skew data. • Granularity is limited. Accuracy of the data derived from IP
14. © AKAMAI - EDGE 2017 Understanding Cellular Visualization with Network Experience Analytics
15. © AKAMAI - EDGE 2017 Cellular visualization Cell ID with good QOS Cell ID with bad QOS
16. © AKAMAI - EDGE 2017 Cellular visualization getting Granular
17. © AKAMAI - EDGE 2017 Data collected and mapped to KPI’s Mobile-Operator Longitude Latitude Connectivity Cell ID Cell ID with low Re- buffer Cell ID with high Re- buffer
18. © AKAMAI - EDGE 2017 Plugin Architecture • Release location lib separately. • Not part of the package but can be enabled by adding location lib. • Customer needs to make API calls for integration
19. © AKAMAI - EDGE 2017 Actionable • Bitrate tuning and Content Encoding • ASN opt in for UDP benefits • Network optimization in ISP • Granular Demographics for Content targeting • QOE score for operators
20. © AKAMAI - EDGE 2017 Ad-Quality • Ad-transcoding • Circuit breaker for Ads • Ad-Targeting
21. © AKAMAI - EDGE 2017 Some ground to cover • Users apprehensive about sharing location data. • App policy and consent for individualization with location. • Permissions for location services. • MA and NEA is not one portal today.
22. © AKAMAI - EDGE 2017 Key Takeaways • Better Prepared. • Granular demographic aggregation. • Accurate content curation.
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24. © AKAMAI - EDGE 2017