Allot continues its spending spree

After the acquisition of Ortiva Wireless, announced in April for $15-$17m, Allot announces today the acquisition of Oversi networks for $16m in cash with a conditional, performance related extra $5m.

Oversi Networks is a provider of transparent caching solutions for OTT and P2P traffic. Specifically, Oversi has been developing a purpose-built video cache, one of the first of its kind.

Many vendors in the space have caches that have been built on open source general-purpose web caches, originally to manage offline video optimization scenarios (for those not able to transcode mp4, flv/f4v containers in real time). As the long tail of video content unfolds, social media and virality create snowballing effects on some video content and a generic web cache shows limitations when it comes to efficiently cache video.

The benefits of a hierarchical, video specific cache then becomes clear. Since video nowadays come in many formats, containers, across many protocols and since content providers repost the same video with different attributes, titles, URLs, duration...etc, it is quite inefficient to cache video only based on metadata recognition. Some level of media inspection is necessary to ascertain what the video is and whether it really corresponds to the metadata.

All in all, another smart acquisition by Allot. On the paper, it certainly strengthens the company position, with technologies compatible and complementary with their legacy portfolio and the recent Ortiva's acquisition. It will be interesting to see how Allot's product portfolio evolves over time and how the different product lines start to synergize.

Edge based optimization part II: Edge packaging

As mentioned in my previous post, as video traffic increases across fixed and mobile networks, innovative companies try to find way to reduce the costs and inefficiencies of transporting large amounts of data across geographies.

One of these new techniques is called edge based packaging and relies on adaptive bit rate streaming. It is particularly well adapted for delivery of live and VOD content (not as much for user-generated content).
 As we have seen in the past, ABR has many pros and cons, which makes the technology useful in certain conditions. For fixed-line content delivery, ABR is useful to account for network variations and provides an optimum video viewing experience. One of the drawback is the cost of operation of ABR, when a video source must be encoded into 3 formats (Flash, Apple and Microsoft) and many target bit rates to accommodate network conditions.

Edge-based packaging allows a server situated in a CDN's PoP in the edge cache to perform manifest manipulation and bit rate encoding directly at the edge. The server accepts 1 file/stream as input and can generate a manifest, rewrap, transmux and protect before delivery. This method can generate great savings on several dimensions.

1. Backhaul. The amount of payload necessary to transport video is drastically reduced, as only the highest quality stream / file travels between core and edge and the creation of the multiple formats and bit rates is performed at the PoP.
2. Storage. Only 1 version of each file / stream needs to be stored centrally. New versions are generated on the fly, per device type when accessed at the edge.
3. CPU. Encoding is now distributed and on-demand, reducing the need for large server farms to encode predictively many versions and formats.

Additionally, this method allows to monetize the video stream:

1. Advertising insertion. Ad insertion can occur at the edge, on a per stream / subscriber / regional basis.
2. Policy enforcement. The edge server can enforce and decide QoE/QoS class of services per subscriber group or per type of content / channel.

Edge based packaging provides all the benefits of broadcast with the flexibility of unicast. It actually transforms a broadcast experience in an individualized, customized, targeted unicast experience. It is the perfect tool  to optimize, control and monetize OTT traffic in fixed line networks.

Edge based optimization part I: RAN traffic optimization

As video traffic grows in mobile and fixed networks alike, innovative companies are looking at optimizing traffic closer to the user. These companies perform loss-less and lossy optimization at the edge of the networks, be it directly in the CDN's PoP or at the RNC in mobile radio networks. We will look today the cellular RAN based optimization and look at Edge optimization in fixed networks in a following post.


As I have indicated in previous posts (here), I believe implementing video lossy optimization in the core network or the backhaul to be very inefficient without a good grasp of what is happening on the user's device or at least in the radio networks. Core network based mobile video optimization vendors infer the state of network congestion by reading and extrapolating the state of the TCP connection. Looking at parameters such as round trip time, packet loss ratio, TCP window, etc... they deduce whether the state of the connection improves or worsens and increase or decrease the rate of optimization. This technique is called Dynamic Bit Rate Adaptation and is one of the most advanced for some of the vendors out there. Others will read the state of the connection at the establishment and will feed and set the encoding rate based on that parameter. 

The problem, with these techniques is that they deal with the symptoms of congestion and not the causes. This leads vendors to taking steps in increasing or reducing the encoded bit rate of the video without understanding what the user is actually experiencing in the field. As you well know, there can be a range of issues affecting the state of a TCP connection, ranging from the device's CPU, its antenna reception, the RAN's sector occupancy from a signalling standpoint, whether the user is moving, etc... that are not actually related to a network payload TCP congestion. Core vendors have no way to diagnose these situations and therefore are treating any degradation of signal as a payload congestion, in some cases creating race conditions and snowball effect where the optimization engine actually contributes to the user experience's degradation rather than improve it.


RAN based optimization vendors are deployed in the RAN, at the RNC or even the base station level and perform a real-time analysis of the traffic. Looking at both payload and signalling per sector, cell, aggregation site, RNC, they can offer a great understanding of what the user is experiencing in real time and whether a degradation in TCP connection is the result of payload congestion, signalling issues or cell handover for instance. This precious data is then analysed, presented and made available for corrective action. Some vendors will provide the congestion indications as a diameter integration, with the information travelling from the RAN to the Core to allow resolution and optimization by the PCRF and the video optimization engine. Some vendors will even provide loss-less and lossy techniques at the RAN level to complement the core capabilities. These can range from payload and DNS deep caching, to TCP tuning, pacing, and content shaping...


This is in my mind a great improvement to mobile networks, allowing to break the barrier between RAN and Core and perform holistic optimization along the delivery chain, where it matters most, with the right information to understand the network's condition.
The next step is having the actual capability to have the device report to the network its reading of the network condition, together with the device state and the video experience to provide feedback loop to the network. The vendors that will resolve the equation device state + RAN condition + Policy management + video optimization = better user experience will strike gold and enable operators to truly monetize and improve mobile video delivery.

Mobile video optimization 2012 - July update

For those who follow the video optimization market, it will not come as a surprise that my acclaimed report needed already an update after its release in March.The market has been abuzz with rumors and movement, following acquisitions, re-positioning and the changes in market share:

• Bytemobile's acquisition by Citrix
• Ortiva wireless acquisition by Allot
• Openwave's acquisition by Marlin Equity Partners
• Mobile video optimization show 2012 in Brussels
• Flash Network now #2 in market share 

The report describes the trends impacting network operators, the technologies involved in video optimization, a review of the vendors and re-sellers in this space, with their differentiators and strategies.


You can find some reviews for the report and my services here and below:

“Patrick is an astute, engaging and articulate individual who has provided my company with valued data, opinion and reports on market status and dynamics in the area of OTT video. Patrick's insights have helped my company recently in developing group strategy and deployment options for video optimization and policy management. ” June 8, 2012
Top qualities: Great Results, Expert, High Integrity
Desmond O'Connor Vice President of Data Design at Deutsche Telekom group