Intelligent media optimization mahindra comviva

Intelligent Media Optimization

Strategies for unlocking the full value of media optimization

SUMMARY

Mobile network operators (MNOs) face a challenging future. Balancing network capacity growth

and revenue growth requires new tools for network operators to improve network efficiency and

performance while creating new revenue opportunities. Media/content optimization (MO) is one

tool that can help, but most MNOs have thus far deployed optimization as a blunt instrument for

capex containment. A more intelligent approach is possible, one that can improve customer

experience and service personalization based on real-time invocation of business rules and

policies.

MO helps MNOs harness the flood of data traffic, especially video, through minimizing network

investments, improving customer experience, and personalizing services. Unfortunately few MNOs

have yet implemented optimization in full support of business goals beyond basic traffic reduction,

not only missing the revenue opportunities it can offer but even negatively affecting revenues.

Intelligent, selective optimization of content based on rules and policies invoked in real time can

both reduce investment costs and provide a tool to raise revenues.

Intelligent MO that supports customers and a spectrum of MNO stakeholders from operations to

marketing and one that is flexible enough to evolve over a carrier’s technology lifecycle (e.g., from

2G to 3G to 4G and from physical to virtual and cloud-based) provides a much better path moving

forward. Ovum sees this as part of a network evolutionary process that will require hooks between

the end-user device, network, optimization, data analytics, and network policy and rules

repositories and enforcement functions.

© Ovum. Unauthorized reproduction prohibited.

THE CURRENT STATE OF CONTENT OPTIMIZATION

Where are MNOs today and why?

Content optimization techniques in use today range from basic compression and transrating, to

more elaborate traffic shaping and caching (especially useful for video, but it can be applied to all

types of web and image traffic). These optimization techniques generally reduce the time for a

video to start and eliminate external network fluctuations that sometimes cause videos to stall.

They also speed up the time for the video to pick up when jumping forward in the video. The

cache responds to the video request much faster than a remote location. The end result is a much

smoother video that starts faster. MNOs are not currently using session shifting (whereby a

session can be started on one device and completed on another), but they do aspire to provide

that feature in the future.

Currently, optimization is applied principally to save network capex, and is often applied blanket

fashion because there has been a lack of real-time congestion detection tools which alert

operators to the state of the network at a per customer, per location level. Moreover, these tools

need to be deployed deep into the network at each of the radio and core network interfaces. This

is a cost-intensive proposition and without supporting use cases such as policy-based

optimization, the ROI must be considered as questionable.

Ovum’s research suggests that media optimization strategies and deployments evolve in three

basic phases, as depicted in Figure 1.

MNOs already have basics in place (which deals with HTTP content optimization and

caching) and now need to adopt more sophisticated approached. In Phase 1, which spans

basic "always on" to more sophisticated approaches, the business goal is reducing or

delaying capex by reducing traffic. Optimization is applied according to the content and

device type, without any additional intelligence (such as network type, network state, the

cell location, the customer’s profile and tariff plan - arguably only unlimited plans can be

optimized without any negative impact on revenue).

Phase 2 focuses on managing traffic to improve customer experience (CE) and

satisfaction and, therefore, reduce churn and other negative business consequences.A

key to improving the quality of customer experience is determining when optimal

conditions occur through historical analysis, and then setting this as the goal baseline.This

calls for an investment in software tools. For example, there are tools emerging that help

operators bridge the gaps between networks, services, and customers using an

automated customer experience management center. This provides quality indices that

can be used to monitor the end-to-end quality of different services.


Phase 3 adds a focus on more personalized, real-time service offers to increase ARPU.

By applying optimization and acceleration techniques during peak hours or when the

network is congested, it becomes ppossible to deliver differentiated service to defined

premium subscribers.

In the field, our research indicates that the goals of many optimization deployments are still at

Phase 1 (reduce traffic 20% or more, typically), with some progression to Phase 2 goals. Indeed,

Phase 1 and 2 goals tend to overlap and intertwine, as MNOs can "dial in" a balance of traffic

reduction and customer experience improvement based on how they "tune" MO across the

network. Although MNOs can see the benefits of moving to Phase 3, they are proceeding

deliberately to test proof-of-concept use cases and avoid any possible collateral brand damage. It

will be a 1-2 year journey.

Figure 1: Intelligent optimization is an evolving process

Source: Ovum

Pitfalls of optimization implemented too broadly and bluntly

Optimization overkill can undercut the MNO’s business strategy

Too many deployed solutions are built on the premise thatthe operator needs to optimize all

content with the goal of freeing up X% (usually 20-30%) of network capacity. Ovum believes this

simple approach can actually undermine the operator’s business plan in two ways:

Overinvestment in optimization technology. Trying to optimize all network content, and

in particular video, can lead to over investment in network optimization solutions and divert

capex investment from areas where it can be better spent. Additionally, some large

Internet content providers are starting to provide their own optimization, such as


converting a video stream from high-definition to standard-definition based on network

conditions, albeit with no link to a subscriber or his/her service plan. Any “over-

optimization” can divert capex investment from areas where it can be better spent.

Negative impact on data revenues. Optimizing all content regardless of network

conditions or subscriber situations can hurt an operator’s ability to fully monetize its mobile

broadband network. For example, if an operator reduces all traffic by 20% the end-user

might be able to suffice on a monthly data service of 1GB. However, if optimization was

more selectively used the end-user could have opted for a more expensive data service

plan.

Finally, basing an optimization strategy purely on freeing up a specific percent of network capacity

is shortsighted and unrealistic. From Ovum’s discussions with mobile operators any capacity made

available by way of optimization will be eventually consumed, either due to the “bigger roads just

invite more cars” phenomenon or because savvy operators actively choose to allocate some

capacity to improve customer experience.


INTELLIGENT OPTIMIZATION MODELS

Intelligent optimization is an evolving process

Moving from simple media optimization to a more intelligent implementation requires that MNOs

develop business policies and rules and invest in Policy and Charging Rules Function (PCRF) and

related capabilities to enable more sophisticated data services. This evolution won’t happen all at

once and it can’t be driven only by the network division:optimization will lead to more individualized

services and therefore higher ARPUs only if the operator’s marketing division is engaged.

Ultimately Ovum sees optimization as part of the closed loop illustrated in Figure 2. In this loop

optimization would have a role in all three areas – collect or ‘capture’ data, analyze data, and act

on data – and could work together with other elements and support systems to provide a

differentiated service. The three elements in the close loop model constitute:

• Collect/Capture: Pull together all relevant network and subscriber (usage) data.

• Analyse: Correlation and draw insights from data on the network, the subscriber, the location

and the access device.

• Act: Create policies for the PCRF, which includes details on location-based traffic policies,

customer spend-based policies, and more traditional parameters such as device, time, volume,

etc.

Ultimately, optimization will become intelligent enough to distinguish a full range of parameters, so

that it is user aware (and takes account of a customer’s profile and their current plan), device

aware, network and network congestion aware, and service aware (to take account of different

application, protocols, and content types).


Figure 2: "Closed loop" allows better match of revenue and performance

Source: Ovum

The benefits of intelligent optimization

Rather than employ “always on, optimize everything” approaches, intelligent network content

optimization works on several levels. It combines traffic steering with real-time network conditions

and network and subscriber rules and policy inputs in a closed loop system to support specific

business goals beyond simple traffic reduction.

With traffic steering, instead of all content automatically being optimized, based on rules and

policies that determine when and what kind of traffic is optimized, specific traffic is routed to the

optimization element. Through intelligent traffic steering, the operator can limit its overall

investment in content optimization and protect the value of its data service plans.

The rules and policies used to properly steer traffic can be fully contained in the optimization

solution or can be tied to other data and rules depositories such as a PCRF. Decisions on traffic

steering can be limited to the state of the network or include subscriber information, such as class

of service, to guide steering decisions. Device-based software clients fill an important role in


providing real-time customer experience KPIs to the operator that can help set rules and policies

and even reduce drive test costs.

With intelligent optimization an operator has the ability to target network congestion relief at

different parts of the network during different times of the day. For example, it can apply

optimization to content heading towards the city center during peak work hours and redirect

optimization towards the suburbs after work. The optimization follows the users rather than be

applied to all areas even during low-usage periods.

Operators can also use intelligent optimization as a tool for supporting different class of customers.

Some customers may be willing to pay more to guarantee a better customer experience for sports

content, for example; the MNO can build an offer around this user segment. MNOs could also use

intelligent content optimization to offer content providers service level agreements (SLAs) around

video delivery.

Implementation challenges of optimization

Currently the operation of MNO organizations is heavily siloed and to ensure the shift from a

network centric approach to a more customer centric approach requires bringing together different

groups within the company, including marketing and customer service. These other organizations

may not be knowledgeable about media optimization or used to working so closely together.Still,

these hurdles are worth tackling: customer requirements will become a primary focus. Ultimately,

this will ensure the success of the closed-loop model and provide a boost to the MNO’s top and

bottom lines.

Product architectures are shifting

Products can be architected as mobile optimization solutions, as single-function products, or as

part of a more broadly integrated solution. The optimization function can be located in a variety of

places in the network. There are two broad architectural variants in the market.

Single-function point product deployments –for example, where deep packet inspection (DPI) and

optimization capabilities are separate and not integrated – will be relegated to the past. Longer-

term trends favor virtualized, cloud-based approaches and commercial off-the-shelf hardware.

Until that time, integrated solutions – where multiple, modular optimization-related tools are

integrated into a single box or blade server – provide some advantages, including:

lower latency and improved QoE through less need to steer video between multiple

nodes.

procurement simplicity (a single product to purchase).

faster deployment through pre-integration (less work for the MNO or system integrator to

do at deployment).


Some mobile network equipment vendors integrate capabilities right into core elements, for

example gateway general packet radio service (GPRS) support node (GGSNs) or packet

gateways (PGWs). To make the most out of the business opportunities, as shown in Figure 1’s

Phase 3, the ultimate integration of optimization is with other network functions, including PCRF,

OSS/BSS, and analytics engines.

Finally, optimization functions can be centralized or decentralized and located as:

mobile core solutions upstream from or integrated with the GGSN.

cloud-based solutions in data centers.

network edge solutions between the radio access network (RAN) and mobile core.

device-based capabilities.

There is no one right architecture or location – each mobile network operator will need to

determine the best mix of capabilities in partnership with its vendors.

Device and cloud-based solutions

Ovum does foresee increased popularity of device-based and cloud-based solutions for better

service personalization and scaling, respectively. Specifically, we anticipate the following

developments:

Client-based software agents: More processing and intelligence is likely to move to the

device, including caching, decryption, pre-fetching of content, policy enforcement, and so

on. Client-based software can also provide content source information on the state of the

network and initiate changes in coding and rating.

On-device monitoring agents: On-device monitoring agents aggregate information on

the network as experienced by the customer and relay information to the network. MNOs

can make use of this information to apply the best optimization policy. These on-device

monitoring agents are less expensive than network probes. Essentially the operator isin

effect able to crowd source information from customers on the state of the network.

Session shifting: These capabilities will enable new mobile operator services. This

would require a client application and a GUI-based menu of content. Video could be

started on one device and continued on another.

Time shifting: This will allow downloading of content during off-peak hours. Subscribers

could be prompted for whether video downloads could be delayed in exchange for a

lower price or other incentive.

Moore's Law in action: As processors get more powerful, more functions will be added

to integrated systems and more systems will be based on COTS hardware.

It’s important that MNOs query prospective vendors regarding future development to make sure

capabilities will evolve in line with their own plans. These vendors fall into three broad categories:


Specialists including video, policy and caching specialists like Vantrix, Openet, PeerApp,

etc. The appeal of these as vendors is that they are designing leading edge technology

which offers MNOs good levels of flexibility, once the tools are well integrated with

existing systems.

Network equipment provider (NEP) vendors including Ericsson, Huawei, ZTE, NSN

and Alcatel Lucent who are collaborating with specialists for select components, e.g.

Ericsson is partnering with Vantrix, Alcatel Lucent with cloud-based video optimization

specialist SkyFire. The benefits of partnering with them is their network expertise aids in

integrating necessary features. They may not be as agile as specialist providers,

however, in addressing customization requirements.

Non-NEPs like Comviva or Allot who are offering optimization solutions that are focused

on a limited set of areas. Total cost of ownership, flexibility to customization needs, and

speed to market are their strengths. Integration challenges may remain and the depth of

network expertise of some vendors could pose challenges for operators.

Recommendations for mobile network operators

Clear business rules and policies are the starting point for a successful

optimization strategy

Optimization products are increasingly capable through integration with rules and policy engines,

traffic steering elements, caching functions, network probes, and other functions. But to truly

benefit from these capabilities MNO stakeholders from multiple departments from operations to

marketing and customer support must work together to establish business goals and priorities and

provide the best balance between capex efficiency, customer experience, and revenue creation.

Vendors can help educate MNO stakeholders on what’s possible through case studies.

Reduce risk through proof-of-concept tests based on use cases

Once business goals are established, focusing on and testing a small number of specific use

cases will boost confidence in projected returns. Take advantage of vendors' service organizations

to help deploy an integrated solution based on specific use cases – don't just deploy a set of

boxes.

Set specific KPI targets to meet business goals

Setting specific targets will help reinforce business goals and monitor results. For example, if the

desired outcome is better customer experience in congestion situations, set specific targets for

transactions per second, MOS scores (mean opinion score, a measure of video quality), download

or page display stats, and so on, and then measure them as objectively as possible. In this

context, there is a genuine need for a cross-functional organizational model involving the office of

the CTO, the CIO, CMO/CSD, and with clearly defined KPIs. The key is to focus on KPIs that

matter to the customer, not just network-centric parameters. Device agents that report real-time


KPIs can help fine-tune policies and rules-based on network, location, subscriber category, device,

application, and congestion.

Deploy tools for real-time and predictive network state modelling

Do this intelligently by crowd-sourcing this information from users.

Be honest with your customers, and don't assume you know best

In a congestion situation, fairness will go a long way. Be as transparent as possible with customers

about what you are doing and why. Give customers choices (e.g. to display video in standard

definition vs. high definition) and a clear sense of the benefits. A clear opt-in choice is better than

an obscure opt-out or a one-size-fits-all automated action. You might even want to consider what it

would take to give customers direct control over invoking optimization.

Make sure your optimization vendor’s roadmap includes new architecture models for better

scalability

Scaling conventional architectures can be challenging. As a result, many of the vendors are

evolving products for virtualized and cloud-based architectures. Some vendors are pushing

network functions virtualization/software-defined networking (NFV/SDN) approach to traffic

steering too. Press vendors for their roadmaps to make sure their solutions will evolve with your

needs.


APPENDIX

This white paper was researched, authored and produced by Ovum in association with Mahindra

Comviva, as part of a series of papers assessing the current state and future market direction of

mobile broadband services for mobile operators.

About Mahindra Comviva

Mahindra Comviva is the global leader in providing mobility solutions. It is a subsidiary of Tech

Mahindra and a part of the USD 16.7 billion Mahindra Group. With an extensive portfolio spanning

mobile finance, content, infotainment, messaging and mobile data solutions, Mahindra Comviva

enables service providers to enhance customer experience, rationalize costs and accelerate

revenue growth. Its mobility solutions are deployed by 130 mobile service providers and financial

institutions in 90 plus countries, transforming the lives of over a billion people across the world. For

more information, please visit www.mahindracomviva.com.

Ovum Consulting

Ovum has an enviable and hard-earned reputation as a provider of telecoms consulting services.

Our consulting customers tell us that, above all else, it is Ovum's industry knowledge and attention

to detail that puts us ahead of our competitors. This is directly related to the expertise of our

consultants and analysts, and the project and research methodologies we use. We work across

the globe with business leaders of telecoms operators, service providers and ICT vendors and with

investment banks, governments and industry regulators. We hope that this analysis will help you

make informed and imaginative business decisions. If you have further requirements, Ovum’s

consulting team may be able to help you. For more information about Ovum’s consulting

capabilities, please contact us directly at [email protected].

Disclaimer

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Intelligent media optimization mahindra comviva

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