Optimizing Over-The-Top Video Delivery

Optimizing Over-The-Top Video Delivery

The rapid growth of online video has created tremendous new opportunities for content providers and mobile network operators (MNOs) to engage with customers and create new revenue streams. However, even although video content is a dominant component of many providers’ online strategies, they face multiple challenges to deliver video to their customers. At times, delivery becomes even more challenging when trying to figure out the key enablers to keep their viewers engaged. And, on top of media streaming, the revenue generated by ad insertion is growing fast and it is vital to push ads based on the viewer’s tastes and needs.

Cisco predicts that live internet video will grow 15-fold from 2016 to 2021 and account for 13% of internet video traffic by 2021. At the same time, consumer video-on-demand (VoD) traffic will nearly double by 2021 when VoD traffic will be equivalent to 7.2 billion DVDs per month.

The proliferation of multimedia handheld devices and network access has led to a big increase in mobile video traffic, which increases demand for network bandwidth. Worldwide, people watch an average of five hours and 45 minutes of online video weekly, which is a 34% increase from 2016, according to a 2017 survey report from Limelight Network. Video quality is a high priority concern for viewers. The current expectation is for 1080p on handsets and 4K on handhelds and larger displays such as smart TVs, with 8K media streaming on the way.

To meet expectations, MNOs need to either compromise the number of simultaneous subscribers or the quality of the subscriber’s experience. Delivering both would be too expensive. At times of high network congestion, operators often fail to deliver consistent and reliable bandwidth to subscribers. This results in the frequent stalling of videos, eventually making the video unwatchable. In the best-case scenario, the video is buffered—that is, delivered at a rate faster than the rate of playback—and eventually discarded if the subscriber abandons the video prematurely. Buffering results in unnecessary streaming of video content and wasted bandwidth.

Each handheld device, PC and TV in the market has its own specifications for device resolution, supported file formats and codecs. That means content providers will not be able to support all the possible combinations of file and codec formats required by their clients.

Service providers’ primary goal is to achieve a very high end-user experience that does not compromise video quality or data security while delivering video to all types of devices and lowering network operational expenses. Creating an optimal video delivery (OVD) solution for over-the-top (OTT) service providers and content providers requires they achieve a certain revenue target.

Cisco predicts that 71% of all global internet traffic will utilize content delivery networks (CDNs) by 2021, up from 52% in 2016. Mobile data traffic—including handset-based data traffic, such as text messaging, multimedia messaging and handset video service—is expected to grow by 46% a year through 2021.

An adaptive streaming media solution that generates media segments based on end-client device capabilities enables OVD and provides the highest level of satisfaction for the viewers. This solution includes server-side functionality that adapts to varying network conditions along with a highly efficient distributed CDN.

Below are six methodologies that can be used on the server side for optimizing media delivery based on the capability of the client’s device without compromising video quality.

■ Client-aware video optimization.  Identifies the device properties through server-side intelligence and optimizes video according to the client specifications such as device resolution and supported file and codec formats.

■ Network-aware video optimization. Calculates the current network congestion and based on the current bandwidth allocated to the subscriber, quality level of the video—the bitrate—is changed dynamically by using Adaptive Transrating technique which changes only the bitrate and leaves the audio-video codecs of the file undisturbed. This means the transmitted video size decreases on low bandwidth and increases on high bandwidth, thereby providing the end user with uninterrupted video playback.

■ Quality-of-Service-based video optimization. Optimization of the video is based on two quality parameters:

  • Create a rate adaptation algorithm that performs network bandwidth and client buffer possession estimations using local information on the server side. This method doesn’t require explicit feedback from the client, so all existing video-player software on the client device is supported.
  • The objective of the rate adaptation algorithm is to prevent playback starvation caused by client buffer underflow. To prevent buffer underflow, the server must estimate the available network bandwidth and the client buffer possession.

■ Quality of Experience (QoE). Improved by identifying the correlation between Network QoS and Applications QoS.

■ Video Pacing. Limiting video transmission based on user interest.

  • Many users switch to other videos before the completion of the current video playback. As a result, close to 80% of internet video ends up being delivered to the end user but are never watched.
  • To avoid such scenarios, video-pacing modules limit video transmission to only the portion of video required for smooth playback, thereby eliminating transmission of unseen video even if the end user has good download speed.

■ Content-aware video optimization and just-in-time video delivery.

  • Existing adaptive streaming servers attempt to deliver the entire video file as fast as possible, regardless of the video’s viewing rate. As a result, the user’s video player buffers the video content and plays it at the encoded video viewing rate. Such content-unaware video delivery causes significant inefficiencies in the network. Existing systems adhere to varying network conditions but abandon content-specific adaptation requirements. Content-specific adaptation ensures both high video quality and reduced data traffic.
  • Content-aware video bitrate throttling optimization delivers video consistently with the viewing rate. It analyzes the encoded video stream and estimates the video content in the buffer of the subscriber device. As a result, users terminate fewer videos prematurely and less content is wasted. At the same time, concurrent video sessions share bandwidth more efficiently.


Comprehensive server-side functionality without much dependence on client-side solutions running on end-user devices shall help achieve optimized video delivery that in-turn helps accelerate and support the growing demand for video content. Above all, personalized ad insertion and subscription-based video-on-demand streaming techniques help expand potential video revenue, which is the topic of the next blog.

■ https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html
■ http://tubularinsights.com/2020-mobile-video-traffic/
■ https://www.limelight.com/resources/white-paper/state-of-online-video-2017/
■ http://www.econtentmag.com/Articles/Editorial/Feature/The-State-of-Online-Video-2018-122575.htm
■ https://en.wikipedia.org/wiki/Video_optimization

About the Author

Suriya Mohan Sekaran

Suriya Mohan Sekaran
Sr Engineering Project Manager


Suriya plays a techno manager role at Aricent and having over 16 years of experience in IoT, OTT, Automotive, Multimedia, Connectivity and Security domains. He has contributed significantly towards proposals, creating PoCs and MVPs (Minimum Viable Product). He is very passionate about technology advancements, current and future trends and spends quality time on knowledge sharing. In his free time, he enjoys traveling.


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