Visualizing Cellular Audience for Streaming KPI's
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This document discusses visualizing streaming performance data for cellular audiences. It notes that more than 75% of viewers now stream from mobile networks. The document outlines how Akamai's Media Analytics and Network Experience Analytics tools can provide granular performance data and metrics on a cellular level, including by cell ID, operator, and location. This level of data insights allows content providers to optimize their streaming for mobile audiences and cellular networks.
Visualizing Cellular Audience for Streaming KPI's
- 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.
- 23. © AKAMAI - EDGE 2017
- 24. © AKAMAI - EDGE 2017