Avvasi VNA provides a rich suite of unparalleled video traffic analytics tools that enables service providers to monitor, store, collate and analyze business intelligence related to mobile video network traffic. This data is available from the Avvasi Q-VUE analytics store to create automated reports or to run interactive, specific queries through the Avvasi reporting client.
Optimization planning and implementation
Optimization solutions are costly and if implemented in haste, can negatively affect subscriber QoE. VNA provides detailed traffic profiles which allow providers to engineer better optimization solutions. An optimization plan can then take aim at reducing network traffic for all users, or can be designed to deliver the best experience possible given network resources. It is also possible to measure the effectiveness of ongoing optimization using Q-VUE to analyze QoE and video traffic analytics to:
- Understand the demographics and QoE of OTT video flowing through the network
- Build a traffic profile to plan for and select the most effective optimization scheme for reducing operating costs
- Use Q-VUE to optimize vendor selection by evaluating each solution independently in a lab or trial environment
- Use Q-VUE to assess the ongoing effectiveness of a chosen optimization scheme
On-deck and OTT service measurement and comparison
Use Q-VUE to measure the performance of an on-deck service offering. This includes monitoring consumption, QoE and frequency of use. QoE intelligence can form the basis of targeted service churn-reduction programs.
Compare consumption patterns and quality levels between the on-deck and OTT content. The broadcast industry has proven that quality expectations rise when subscribers are required to pay for content. Therefore, the QoE of premium on-deck services should be better than the QoE of free OTT content. In addition to quality levels, the quantity of video streamed from OTT vs on-deck can provide useful service planning information. Time of day patterns can reveal which sites drive the most traffic compared to others.
Measure and compare:
- traffic trends
- viewer habits such as dwell time, resolution
- consumption trends
- most popular content
- QoE by site/location, time of day, or video demographics such as resolution, format, duration or device type
Avvasi VNA provides a comprehensive suite of analytics tools to identify otherwise hidden usage trends that:
- highlight devices or services that drive the most video usage
- enable understanding of video QoE, for different devices or services
- provide understanding of how different devices or services affect the network in terms of video traffic
OTT business intelligence
In many instances, the proliferation of OTT video consumption by mobile subscribers has occurred so quickly that it has left providers scrambling to understand the dynamics of the traffic. The first step is frequently one of gaining insight into both instantaneous and trending usage patterns in order to determine the best response to handle the growth.
- Measurement of all OTT video traffic across the network in terms of streaming protocols, containers, codecs, video quality and resolutions
- Quality scores for specific content such as Netflix and YouTube, or across all OTT traffic sources
- Traffic demographics through drill-down, fine-grain reports
- Traffic trends plotted as line or area charts, over time
Detailed awareness of video traffic as it flows through the network enhances the ability of operational staff to perform effective infrastructure capacity planning. Degraded QoE measurements can help as they provide data points to identify and correct potential trouble spots in the network. QVUE can also help identify which content sites generate the most amount of traffic.
Service providers require the ability to visualize the instantaneous and trending flow of video through their infrastructure, including
- Aggregate bandwidth consumed by sessions with poor or low QoE, which is in effect wasted bandwidth
- Areas of cell congestion caused by video traffic flows
- Impact of cell congestion on video QoE
- Time-of-day or day-of-week hot spots
Profile demographic data
- Media sources
- Streaming protocols
- Devices and screen resolutions
- Network topology (cell site)
- Media stream bit rates, frame rates, etc
- Content viewing trends
- Stream duration
- Video QoE