Harkeerat Bedi, Research Scientist
In the early days of video streaming, viewers were willing to endure a frustrating playback experience to gain access to exclusive content. As the number of content providers sharing their content among multiple distributors has grown, Quality of Experience (QoE) has become vital to viewer retention.
Quality of Experience refers to the overall experience of a user watching a video stream. Unlike Quality of Service (QoS), QoE is a more subjective matter, thus difficult to measure, or to guarantee a certain quality level. QoE is made up of many key performance indicators (KPIs) that video services track to gain clarity of their platform’s performance. These quality metrics can be broken down into more specific areas of concern such as rebuffering or extensive bitrate fluctuation.
Of the various metrics, rebuffering is the most noticeable and annoying fault for viewers. That little spinning wheel is the symbol for a bad viewer experience. Video industry research consistently shows that viewers quickly abandon a stream when they experience rebuffering. The blame for rebuffering and a degraded QoE can be difficult to pinpoint and could stem from any number of sources across the viewer’s Internet Service Provider (ISP), the content delivery network (CDN), the client’s browser/player app, or the original publisher’s video infrastructure.
While problems with the ISP or the publisher are largely out of our control, we are now able to capture actionable data that enables us to identify and resolve QoE issues stemming from the CDN. To do this, we’ve developed an algorithm we call “Estimate Rebuffer” to identify video QoE issues using web server logs. This real-time monitoring system uses granular data to identify a range of QoE issues and drill down to understand root causes and corresponding resolution actions. In this post, we’ll look at how this algorithm works to determine QoE problems and how we’re able to use it to improve QoE.
Check out the full blog now on Medium.