Policy driven optimization

The video optimization market is still young, but with over 80 mobile networks deployed globally, I am officially transitioning it from emerging to growth phase in the technology life cycle matrix.


Mobile world congress brought many news in that segment, from new entrants, to networks announcements, technology launches and new partnerships. I think one of the most interesting trend is in the policy and charging management for video.


Operators understand that charging models based on pure data consumption are doomed to be hard to understand for users and to be potentially either extremely inefficient or expensive. In a world where a new iPad can consume a subscriber's data plan in a matter of hours, while the same subscriber could be watching 4 to 8 times the same amount of video on a different device, the one-size-fits-all data plan is a dangerous proposition.


While the tool set to address the issue is essentially in place, with intelligent GGSNs, EPCs, DPIs, PCRFs and video delivery and optimization engine, this collection of devices were mostly managing their portion of traffic in a very disorganized fashion. Access control at the radio and transport layer segregated from protocol and application, accounting separated from authorization and charging...
Policy control is the technology designed to unify them and since this market's inception, has been doing a good job of coordinating access control, accounting, charging, rating and permissions management for voice and data.


What about video?
The diameter Gx interface is extensible, as a semantics to convey traffic observations and decisions between one or several policy decision points and policy enforcement points. The standards allows for complex iterative challenges between end points to ascertain a session's user, its permissions and balance as he uses cellular services. 
Video was not a dominant part of the traffic when the policy frameworks were put in place, and not surprisingly, the first generation PCRFs and video optimization deployments were completely independent. Rules had to be provisioned and maintained in separate systems, because the PCRF was not video aware and the video optimization platforms were not policy aware.
This led to many issues, ranging from poor experience (DPI instructed to throttle traffic below the encoding rate of a video), bill shock (ill-informed users blow past their data allowance) to revenue leakage (poorly designed charging models not able to segregate the different HTTP traffic).


The next generation networks see a much tighter integration between policy decision and policy enforcement for the delivery of video in mobile networks. Many vendors in both segments collaborate and have moved past the pure interoperability testing to deployments in commercial networks. Unfortunately, we have not seen many proof points of these integration yet. Mostly, it is due to the fact that this is an emerging area. Operators are still trying to find the right recipe for video charging. Standards do not offer guidance for specific video-related policies. Vendors have to rely on two-ways (proprietary?) implementations.


Lately, we have seen the leaders in policy management  and video optimization collaborate much closer to offer solutions in this space. In some cases, as the result of being deployed in the same networks and being "forced" to integrate gracefully, in many cases, because the market enters a new stage of maturation. As you well know, I have been advocating a closer collaboration between DPI, policy management and video optimization for a while (here, here and here for instance). I think these are signs of market maturation that will accelerate concentration in that space. There are more and more rumors of  video optimization vendors getting closer to mature policy vendors. It is a logical conclusion for operators to get a better integrated traffic management and charging management ecosystem centered around video going forward. I am looking forward to discussing these topics and more at Policy Control 2012 in Amsterdam, April 24-25.

Mobile video QOE part I: Subjective measurement

As video traffic continues to flood many wireless networks, over 80 mobile network operators have turned towards video optimization as a means to reduce the costs associated with growing their capacity for video traffic.

In many cases, the trials and deployments I have been involved in, have shown many carriers at a loss when it comes to comparing one vendor or technology against another. Lately, a few specialized vendors have been offering video QoE (Quality of Experience) tools to measure the quality of the video transmitted over wireless networks. In some cases, the video optimization vendors themselves have as well started to package some measurement with their tool to illustrate the quality of their encoding.

In the next few posts,and in more details, in my report "Video Optimization 2012" I examine the challenges and benefits of measuring  the video QoE in wireless networks, together with the most popular methods and their limitations.

Video QoE subjective measurement

Video quality is a very subjective matter. There is a whole body of science dedicated to provide an objective measure for a subjective quality. The attempt, here, is to rationalize the differences in quality between two videos via a mathematical measurement. It is called objective measurements and will be addressed in my next posts. Subjective measurement on the other hand, is a more reliable means to determine a video’s quality. It is also the most expensive and the most time-consuming technique if performed properly. 

For video optimization, a subjective measurement usually necessitates a focus group who is going to be shown several versions of a video, at different quality (read encoding). The individual opinion of the viewer is recorded in a templatized feedback form and averaged. For this method to work, all users need to see the same videos, in the same sequence, with the same conditions. It means that if the videos are to be streamed on a wireless network, it should be over a controlled environment, so that the same level of QoS is served for the same videos. You can then vary the protocol by having users comparing the original video with a modified version, both played at the same time, on the same device, for instance.

The averaged opinion, the Mean Opinion Score, of each video is then used to rank the different versions. In the case of video optimization, we can imagine an original video encoded at 2Mbps, then 4 versions provided by each vendor at 1Mbps, 750kbps and 500kbps and 250kbps. Each of the subject in the focus group will rank each version from each vendor from 1 to 5, for instance.

The environment must be strictly controlled for the results to be meaningful. The variables must be the same for each vendor, e.g. all performing transcoding in real time or all offline, same network conditions, for all the playback / streams and of course, same devices and same group of users.

You can easily understand that this method can be time consuming and costly, as network equipment and lab time must be reserved, network QoS must be controlled, focus group must be available for the duration, etc...

In that example, the carrier would have each corresponding version from each vendor compared in parallel for the computation of the MOS.  The result could be something like this:

The size of the sample (the number of users in the focus group) and how controlled the environment is, can dramatically affect the result, and it is not rare that you find aberrational results, as in the example above where vendor "a" sees its result increase from version 2 to 3.

If correctly executed, this test can track the relative quality of each vendor at different level of optimization. In this case, you can see that vendor "a" has a high level of perceived quality at medium-high bit rates but performs poorly at lower bit rates. Vendor "b" shows little degradation as the encoding decreases, vendors "c" and "d" show near-linear degradation inversely proportional to the encoding.

In every case, the test must be performed in a controlled environment to be valid. Results will vary sometimes greatly from one vendor to an other, and sometimes with the same vendor at different bit rate, so an expert in video is necessary to create the testing protocol, evaluate the vendors' setup, analyse the results and interpret the scores. As you can see, this is not an easy task and rare are the carriers who have successfully performed subjective analysis with meaningful results for vendor evaluation. This is why, by and large, vendors and carriers have started to look at automatized tools to evaluate existing video quality in a given network,  to compare different vendors and technologies and to measure ongoing perceived quality degradation due to network congestion or destructive video optimization. This will be subject of my next posts.

Openwave for sale, Sandvine's buyback, Comverse's Spin-off

Openwave Systems Inc. (Nasdaq: OPWV): 
Openwave announced today that their board of directors has decided to "pursue strategic alternatives"  for the company's mediation and messaging products business.
While this is hardly a surprise, if you have followed the saga over the last year (here, here, here, here and here) it is till sad to see one of the great companies who shaped the mobile internet divest their assets. The company is not completely up for sale, only the product business is, while the board and management team are trying to monetize further their patent portfolio through licensing deals, such as the one with Microsoft, that brought $m18 last quarter.
For a full list of potential acquirer of Openwave assets, don't hesitate to contact me through linked in or my email, at the top right of this page.




Sandvine Corporation (TSX:SVC) (AIM:SAND):
On the heels of reporting their Q4 and fiscal 2011 results ( Q411:$20.6 million revenue GAAP net loss of 3.6 million (non-GAAP¹ loss of $2.8 million); fiscal 2011 revenue $89.3 million and GAAP net loss 5.8 million (non-GAAP¹: $2.2 million loss)) and 44 new customers, announced that its Board of Directors has approved the adoption of an open market stock buyback program for the purchase of up to approximately 12 million common shares ("Shares") over a one-year period.


 Comverse Technology(CMVT)
Comverse technology is the holding structure behind Comverse and Verint. It has announced that it will distribute the share of its wholly owned subsidiary Comverse to their shareholders on a prorata basis. The move is an effort to create a more tax efficient structure and unlock some of the value. The investors welcomed the news with disappointment as the were hoping for a full buyout through M&A.