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QoE in adaptive video streaming

OTT streaming services are exploding. Hundreds of millions of people worldwide have already subscribed to one or more streaming services. Unlike traditional Pay TV services, subscribers expect to be able to consume streaming services on any device, including TV, smartphone, PC, tablet, and in any location (home, office, on the way to and from work, etc.).  While content is definitely ‘king’, delivering premium quality is a close second, and is a major concern for streaming service providers. Annoying experiences like rebuffering or slow start times can lead to lost subscribers and lost advertising revenue. To learn more about key metrics that affect users' Quality of Experience (QoE) in adaptive video streaming, view this blog post. ​​

The ‘last-mile’ challenge

CDNs ensure video clients receive content from the closest available cache, but often this does not solve all the issues of quality of experience.


Delivering an Adaptive Bit Rate stream from the edge of the CDN to video clients installed on end-devices, i.e. across the ’last-mile’ network, is the most challenging delivery stage in terms of QoE.  Last mile network conditions are often volatile and highly dynamic:

  • Different technologies – Clients are connected using DSL, Cable, cellular and Wi-Fi technologies that exhibit different characteristics in terms of available bandwidth, stability, delay and jitter.

  • Congestion on ISP and residential networks - IP packets carrying video streams can be delayed or even discarded.​


Due to fluctuating network bandwidth on the ‘last-mile’, ABR clients often select low video frame resolutions and sometimes buffer up to 30 seconds of video (3 full ABR segments) before starting playback.  This can reduce the re-buffering ratio but increases end-to-end latency to levels that are not acceptable for live events, such as sports. The root-cause for this sub-optimal video QoE in the last-mile are the deficiencies of the transport layer congestion-control (TCP in the case of HTTP based streaming).

Our Product

Compira Labs provides a software solution that dramatically improves QoE in ‘last-mile’ delivery. By upgrading the network stack at the CDN edge nodes, Compira’s next-generation congestion-control technology maximizes media delivery rates and reduces rebuffering ratio and latency.  Data collection and analytics engines provide visibility into QoE metrics across the network, and machine-learning-based intelligence enables continuous performance optimization.

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Solution components

  • Kernel Module (KM) - Installable Linux kernel module providing real-time streaming optimization via next-generation congestion-control technology.

  • Thin Agent (TA) –  Installable user-space agent for data-collection and remote kernel-module configuration.

  • Compira Stream – A central web application that activates, configures, and collects data from all agents and modules. Provides performance analytics and non-real-time optimization.

Significantly better QoE

Seamless upgrade transparent to video clients, video applications, and the network
Encoding and format (HLS, MPEG-DASH) agnostic
Enhanced visibility into QoE
(real-time and history)
Customizable QoE
for SVoD and Live
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