O-RAN alliance rApps and xApps typology

 An extract from the Open RAN RIC and Apps report and workshop.

1.    O-RAN defined rApps and xApps

1.1.        Traffic steering rApp and xApp

Traditional RAN provides few mechanisms to load balance and force traffic on specific radio paths. Most deployments see overlaps of coverage between different cells in the same spectrum, as well as other spectrum layered in, allowing performance, coverage, density and latency scenarios to coexist. The methods by which a UE is connected to a specific cell and a specific channel are mostly static, based on location of the UE, signal strength, service profile and the parameters to handover a connection from one cell to another, or within the same cell from one bearer to another or from one sector to another. The implementation is vendor specific and statically configured.

Figure 9: Overlapping cells and traffic steering

Non-RT RIC and rApps offer the possibility to change these handover and assignments programmatically and dynamically, taking advantage of policies that can be varied (power optimization, quality optimization, performance or coverage optimization…) and that can change over time. Additionally, the use of AI/ML technology can provide predictive input capability for the selection or creation of policies allowing a preferable outcome.

The traffic steering rApp is a means to design and select traffic profile policies and to dynamically allow the operator to instantiate these policies, either per cell, per sector, per bearer or even per UE or per type of service. The SMO or the Non-RT RIC collect RAN data on traffic, bearer, cell, load, etc. from the E2 nodes and instruct the Near-RT RIC to enforce a set of policies through the established parameters.

1.2.       QoE rApp and xApp

This rApp is assuming that specific services such as AR/VR will require different QoE parameters that will need to be adapted in a semi dynamic fashion. It proposes the use of AI/ML for prediction of traffic load and QoE conditions to optimize the traffic profiles.

UE and network performance data transit from the RAN to the SMO layer over the O1 interface, QoE AI/ML models are trained, process the data and infer the state and predict its evolution over time, the rApp transmits QoE policy directives to the Near-RT RIC via the Non-RT RIC.

1.3.       QoS based resource optimization rApp and xApp

QoS based resource optimization rApp is an implementation of network slicing optimization for the RAN. Specifically, it enables the Non-RT RIC to guide the Near-RT RIC in the allocation of Physical Resource Blocks to a specific slice or sub slice, should the Slice Level Specification not be satisfied by the static slice provisioning.

1.4.       Context-based dynamic handover management for V2X rApp and xApp

Since mobile networks have been designed for mobile but relatively low velocity users, the provision of high speed, reliable mobile service along highways requires specific designs and configurations. As vehicles become increasingly connected to the mobile network and might rely on network infrastructure for a variety of uses, Vehicle to infrastructure (V2X) use cases are starting to appear primarily as research and science projects. In this case, the App is supposed to use AI/ML models to predict whether a UE is part of a V2X category and its trajectory in order to facilitate cell handover along its path.

1.5.       RAN Slice Assurance rApp and xApp

3GPP has defined the concept of creating a connectivity product with specific attributes (throughput, reliability, latency, energy consumption) applicable to specific devices, geographies, enterprises… as slices. In an O-RAN context, the Non-RT RIC and Near-RT RIC can provide optimization strategies for network slicing. In both cases, the elements can monitor the performance of the slice and perform large or small interval adjustments to stay close the slice’s Service Level Agreement (SLA) targets.

Generally speaking, these apps facilitate the allocation of resource according to slice requirements and their dynamic optimization over time.

1.6.       Network Slice Instance Resource Optimization rApp

The NSSI rApp aims to use AI/ML to model traffic patterns of a cell through historical data analysis. The model is then used to predict network load and conditions for specific slices and to dynamically and proactively adjust resource allocation per slice.

1.7.       Massive MIMO Optimization rApps and xApps

Massive MIMO (mMIMO) is a key technology to increase performance in 5G. It uses complex algorithms to create signal beams which minimize signal interference and provide narrow transmission channels. This technology, called beamforming can be configured to provide variations in the vertical and horizontal axis, azimuth and elevation resulting in beams of different shapes and performance profiles. Beamforming and massive MIMO are a characteristic of the Radio Unit, where the DU provides the necessary data for the configuration and direction of the beams.

In many cases, when separate cells overlap a given geography, for coverage or density with either multiple macro cells or macro and small cells mix, the mMIMO beams are usually configured statically, manually based on the cell situation. As traffic patterns, urban environment and interference / reflection, change, it is not rare that the configured beams lose efficiency over time.

In this instance, the rApp collects statistical and measurement data of the RAN to inform a predictive model of traffic patterns. This model, in turn informs a grid of beams that can be applied to a given situation. This grid of beams is transmitted to the DU through the Near-RT RIC and a corresponding xApp, responsible for assigning the specific PRB and beam parameters to the RU. A variant of this implementation does not require grid of beams or AI/ML, bit a list of statically configured beams that can be selected based on specific threshold or RAN measurements.

Additional apps leveraging unique MIMO features such as downlink transmit power, Multiple User MIMO and Single User MIMO allow, by reading UE performance to adjust the transmit power or the beam parameters to improve the user experience or the overall spectral efficiency.

1.8.       Network energy saving rApps and xApps

These apps are a collection of methods to optimize power consumption in the open RAN domain.

    Carrier and cell switch off/on rApp:

A simple mechanism to identify within a cell the capacity needed and whether it is possible to reduce the power consumption by switching off frequency layers (carriers) or the entire cell, should sufficient coverage / capacity exist with other adjoining overlapping cells. AI/ML model on the Non- RT RIC might assist in the selection and decision, as well as provide a predictive model. The prediction in this case is key, as one cannot simply switch off a carrier or a cell without gracefully hand over its traffic to an adjoining carrier or cell before to reduce quality of experience negative impact.

    RF Channel reconfiguration rApp:

mMIMO is achieved by the combination of radiating elements to form the beams. A mMIMO antenna array 64 64 transceivers and receivers (64T64R) can be configured to reduce its configuration to 32, 16 or 8 T/R for instance, resulting in a linear power reduction. An AI/ML model can be used to determine the optimal antenna configuration based on immediate and predictive traffic patterns.

Mobile Edge Computing 2016 is released!

5G networks will bring extreme data speed and ultra low latency to enable Internet of Things, autonomous vehicles, augmented, mixed and virtual reality and countless new services.

Mobile Edge Computing is an important technology that will enable and accelerate key use cases while creating a collaborative framework for content providers, content delivery networks and network operators. 

Learn how mobile operators, CDNs, OTTs and vendors are redefining cellular access and services.

Mobile Edge Computing is a new ETSI standard that uses latest virtualization, small cell, SDN and NFV principles to push network functions, services and content all the way to the edge of the mobile network. 

This 70 pages report reviews in detail what Mobile Edge Computing is, who the main actors are and how this potential multi billion dollar technology can change how OTTs, operators, enterprises and machines can enable innovative and enhanced services.

Providing an in-depth analysis of the technology, the architecture, the vendors's strategies and 17 use cases, this first industry report outlines the technology potential and addressable market from a vendor, service provider and operator's perspective.

Table of contents, executive summary can be downloaded here.

Mobile QoE White Paper

Extracted from the white paper "Mobile Networks QoE" commissioned by Accedian Networks. 

2016 is an interesting year in mobile networks.  Maybe for the first time, we are seeing tangible signs of evolution from digital services to mobile-first. As it was the case for the transition from traditional services to digital, this evolution causes disruptions and new behavior patterns in the ecosystem, from users to networks, to service providers.

Take for example social networks. 47% of Facebook users access the service exclusively through mobile and generate 78% of the company’s ad revenue. In video streaming services, YouTube sees 50% of its views on mobile devices and 49% Netflix’ 18 to 34 years old demographics watch it on mobile.

This extraordinary change in behavior causes unabated traffic growth on mobile networks as well a changes in the traffic mix. Video becomes the dominant use that pervades every other aspect of the network. Indeed, all involved in the mobile value chain have identified video services as the most promising revenue opportunity for next generation networks. Video services are rapidly becoming the new gold rush.

“Video services are the new gold rush”

Video is essentially a very different animal from voice or even other data services. While voice, messaging and data traffic can essentially be predicted fairly accurately as a function of number and density of subscribers, time of day and busy hour patterns, video follows a less predictable growth. There is a wide disparity in consumption from one user to the other, and this is not only due to their viewing habits. It is also function of their device screen size and resolution, the network that they are using and the video services they access. The same video, viewed on a social sharing site on a small screen or on full HD or at 4K on a large screen can have a 10 -20x impact on the network, for essentially the same service.

Video requires specialized equipment to manage and guarantee its quality in the network, otherwise, when congestion occurs, there is a risk that it consumes resources effectively denying voice, browsing, email and other services fair (and necessary) access to the network.

This unpredictable traffic growth results in exponential costs for networks to serve the demand.

As mobile becomes the preferred medium to consume digital content and services, Mobile Network Operators (MNOs), whose revenue was traditionally derived from selling “transport,” see their share squeezed as subscribers increasingly value content and have more and more options in accessing it. The double effect of the MNOs’ decreasing margins and increasing costs forces them to rethink their network architecture.

New services, on the horizon such as Voice and Video over LTE (VoLTE & ViLTE), augmented and virtual reality, wearable and IoT, automotive and M2M will not be achievable technologically or economically with the current networks.

Any architecture shift must not simply increase capacity; it must also improve the user experience. It must give the MNO granular control over how services are created, delivered, monitored, and optimized. It must make best use of capacity in each situation, to put the network at the service of the subscriber. It must make QoE — the single biggest differentiator within their control — the foundation for network control, revenue growth and subscriber loyalty.

By offering exceptional user experience, MNOs can become the access provider of choice, part of their users continuously connected lives as their trusted curator of apps, real-time communications, and video.

“How to build massively scalable networks while guaranteeing Quality of Experience?”

As a result, the mobile industry has embarked on a journey to design tomorrow’s networks, borrowing heavily from the changes that have revolutionized enterprise IT departments with SDN (Software Defined Networking) and innovating with 5G and NFV (Networks Functions Virtualization) for instance. The target is to emulate some of the essential attributes of innovative service providers such as Facebook, Google and Netflix who have had to innovate and solve some of the very same problems.

QoE is rapidly becoming the major battlefield upon which network operators and content providers will differentiate and win consumers’ trust.  Quality of Experience requires a richly instrumented network, with feedback telemetry woven through its fabric to anticipate, detect, measure any potential failure.

Building a mobile video delivery network? part III

Part I
Part II

Content providers and aggregators have obviously an interest (and in some case a legal obligation) to control the quality of the content they sell to a consumer. Without owning networks outright to deliver the content, they rent capacity, under specific service level agreements to deliver this content with managed Quality of Experience. When the content is delivered over the “free” internet or a mobile network, there is no QoE guarantee. As a result, content providers and aggregators tend to “push the envelope” and grab as much network resource as available to deliver a video stream, in an effort to equate speed and capacity to consumer QoE. This might work on fixed networks, but in mobile, where capacity is limited and variable, it causes congestion.

Obviously, delegating the selection of the quality of the content to a device should be a smart move. Since the content is played on the device, this is where there is the clearest understanding of instantaneous network capacity or congestion. Unfortunately, certain handset vendors, particularly those coming from the consumer electronics world do not have enough experience in wireless IP for efficient video delivery. Some devices for instance will go and grab the highest capacity available on the network, irrespective of the encoding of the video requested. So, for instance if the capacity at connection is 2Mbps and the video is encoded at 1Mbps, it will be downloaded at twice its rate. That is not a problem when the network is available, but as congestion creeps in, this behaviour snowballs and compounds congestion in embattled networks.

As more and more device manufacturers coming from the computing world (as opposed to mobile) enter the market with smartphones and tablets, we see wide variations in the implementation of their native video player.

Consequently, operators are looking at way to control video traffic as a means to maybe be able to monetize it differently in the future. Control can take many different aspects and rely on many technologies ranging from relatively passive to increasingly obtrusive and aggressive.

In any case, the rationale for implementing video control technologies in mobile networks goes beyond the research for the best delivery model. At this point in time, the actors have equal footing and equal interest in preserving users QoE. They have elected to try and take control of the value chain independently. This has resulted in a variety of low level battles, where each side is trying to assert control over the others.

The proofs of these battles are multiple:

• Google tries to impose VP9 as an alternative to H.265 /HEVC: While the internet giant rationale to provide a royalty-free codec as the next high efficiency codec seems innocuous to some, it is a means to control the value chain. If content providers start to use VP9 instead of H.265, Google will have the means to durably influence the roadmap to deliver video content over the internet.
• Orange extracts peering fees from Google / YouTube in Africa: Orange as a dominant position for mobile networks and backhaul in Africa and has been able to force Google to the negotiating table and get them to pay peering fee for delivering YouTube over wireless networks. A world’s first.
• Network operators implement video optimization technologies: In order to keep control of the OTT videos delivered on their networks, network operators have deployed video optimization engine to reduce the volume of traffic, to alleviate congestion or more generally to keep a firmer grip on the type of traffic transiting their networks.
• Encryption as an obfuscation mechanism: Content or protocol encryption has traditionally been a means to protect sensitive content from interception, reproduction or manipulation. There is a certain cost and latency involved in the encoding and decoding of the content, so it has remained mostly used for premium video. Lately, content providers have been experimenting with the delivery of encrypted video as a means to obfuscate the traffic and stop network operators from interfering with it.
• Net neutrality debate, when pushed by large content providers and aggregators is oftentimes a proxy for commercial battle. Th economics of the internet have evolved from browsing to streaming and video has disrupted the models significantly. The service level agreements put in place by the distribution chains (CDNs, peering points...) are somewhat inadequate for video delivery.

We could go on and on listing all the ways that content providers and network operators are probing each other’s capacity to remain in control of the user’s video experience. Ultimately, these initiatives are isolated but are signs of large market forces trying to establish dominance over each other. So far, these manoeuvres have reduced the user experience. The market will settle in a more collaborative mode undoubtedly as the current behaviour could lead to mutually assured destruction. The reality is simple. There is a huge appetite for online video. An increasing part of it takes place on mobile devices, on cellular networks. There is money to be made if there is collaboration, the size of the players is too large to establish a durable dominance without vertical integration.

Building a mobile video delivery network? part II

Part I

Frequently, in my interactions with vendors and content providers alike, the same questions are brought up. Why aren’t content providers better placed to manage the delivery of the content they own rather than network operators? Why are operators implementing transcoding technologies in their networks, when content providers and CDN have similar capabilities and a better understanding of the content they deliver? Why should operators be involved in controlling the quality of a content or service that is not on their network?

In every case, the answer is the same. It is about control. If you look at the value chain of delivering content over wireless networks, it is clear that technology abounds when it comes to controlling the content, its quality, its delivery and its associated services at the device, in the network, in the CDN and at the content provider. Why are all the actors in the delivery chain seemingly hell-bent on overstepping each other’s boundary and wrestle each other’s capacity to influence content delivery?

To answer this question, you need to understand how content used to be sold in mobile networks. Until fairly recently, the only use case of “successful” content being sold on mobile networks was ringtones. In order to personalize your phone, one use to go to their operator’s portal and buy a ringtone to download to one’s device. The ringtones were sold by the operator, charged on one’s wireless bill, provided by an aggregator, usually white-labelled who would receive a percentage of the sale, and then kick back another percentage of their share to the content provider itself who created the ringtone.

That model was cherished by network operators. They had full control of the experience, selecting themselves the content aggregator, in some case the content providers, negotiating the rates from a position of power, and selling to the customer under their brand, in their branded environment, on their bills.

This is a long way from today’s OTT, where content and services are often free for the user, monetized through advertisement or other transparent scheme, with content selected by the user, purchased or sourced directly on the content provider’s site, with no other involvement from the network operator than the delivery itself. These OTT (Over-The-Top) services threaten the network operator’s business model. Voice and messaging are the traditional revenue makers fro operators and are decreasing year over year in revenue, while increasing on volume due to the fierce competition of OTT providers. These services remain hugely profitable for networks and technology has allowed great scalability with small costs increments, promising healthy margins for a long while. Roaming prices are still in many cases extortionate. While some legislators are trying to get users fairer prices, it will be a long time before they disappear altogether.

Data, in comparison, is still uncharted territory. Until recently, the service was not really monetized, used as an appeal product to entice consumers to sign for longer term contracts. This is why so many operators initially launched unlimited data services. 3G, and more recently LTE have seen the latest examples of operators subsidizing data services for customer acquisition.

The growth of video in mobile networks is upsetting this balance though. The unpredictability and natural propensity of video to expand and monopolize network resources makes it a more visible and urgent threat as an OTT service. Data networks have greatly evolved with LTE with better capacity, speed and latency than 3G.  But the price paid to increase network capacity is still in the order of billions of dollars, when one has to take into account spectrum, licenses, real estate and deployment. Unfortunately, the growth in video in term of users, usage and quality outstrips the progress made in transport technology. As a result, when network operators look at video compounded annual growth rate exceeding 70%, they realize that serving the demand will continue to be a costly proposition if they are not able to control or monetize it. This is the crux of the issue. Video, as part of data is not today charged in a very sophisticated manner. It is either sold as unlimited, as a bucket of usage and/or speed. The price of data delivery today will not cover the cost of upgrading network capacity in the future if network operators cannot control better video traffic.

Additionally, both content providers and device vendors have diametrically opposed attitude in this equation. Device manufacturers, mobile network operators and content providers all want to deliver the best user experience for the consumer. The lack of cooperation between the protagonists in the value chain results paradoxically in an overall reduced user experience.

Data traffic optimization feature set

Data traffic optimization in wireless networks has reached a mature stage as a technology . The innovations that have marked the years 2008 – 2012 are now slowing down and most core vendors exhibit a fairly homogeneous feature set. 

The difference comes in the implementation of these features and can yield vastly different results, depending on whether vendors are using open source or purpose-built caching or transcoding engines and whether congestion detection is based on observed or deduced parameters.

Vendors tend nowadays to differentiate on QoE measurement / management, monetization strategies including content injection, recommendation and advertising.

Here is a list of commonly implemented optimization techniques in wireless networks.

•  TCP optimization
• Buffer bloat management
• Round trip time management
• Web optimization
• GZIP
•  JPEG / PNG… transcoding
• Server-side JavaScript
• White space / comments… removal
• Lossless optimization
• Throttling / pacing
• Caching
• Adaptive bit rate manipulation
• Manifest mediation
• Rate capping
• Lossy optimization
• Frame rate reduction
• Transcoding
• Online
• Offline
• Transrating
• Contextual optimization
• Dynamic bit rate adaptation
• Device targeted optimization
• Content targeted optimization
• Rule base optimization
• Policy driven optimization
• Surgical optimization / Congestion avoidance
• Congestion detection
• TCP parameters based
• RAN explicit indication
• Probe based
• Heuristics combination based
• Encrypted traffic management
• Encrypted traffic analytics
• Throttling / pacing
• Transparent proxy
• Explicit proxy
• QoE measurement
• Web
• page size
• page load time (total)
• page load time (first rendering)
• Video
• Temporal measurements
• Time to start
• Duration loading
• Duration and number of buffering interruptions
• Changes in adaptive bit rates
• Quantization
• Delivery MOS
• Spatial measurements
• Packet loss
• Blockiness
• Blurriness
• PSNR / SSIM
• Presentation MOS

An explanation of each technology and its feature set can be obtained as part of the mobile video monetization report series or individually as a feature report or in a workshop.

Building a Mobile Video Delivery Network?

Part II
Part III
In 2014, mobile video is a fact of life. It has taken nearly 5 years for the service to transition from novelty to a growing habit that is quickly becoming an everyday occurrence in mature markets. Nearly a quarter of YouTube and Netflix views nowadays are on a tablet or a smartphone. Of course, users predominantly still stream over wifi, but as LTE slowly progresses across markets, users start to take for granted the network capacity to deliver video.

Already, LTE networks start to show signs of weariness as video threatens the infrastructure and the business model of mobile content delivery.

For those who are familiar with my blog, I have been complaining for a while that mobile carriers are not doing enough to make their networks more video capable. You would think that with anywhere between 40 to 70% of the data traffic, video would warrant more interest and effort than what we see today. Many studies show that although video is the dominant and fastest growing application in mobile, its service quality is mediocre. Conviva claims that about 15% of videos in wifi and cellular networks never actually start, while Skyfire shows that close to 50% of consumers experience video problems “often” or “all the time” in the US.

Of course, part of the issue here is that 85% of these videos streamed over mobile networks are from OTT properties. In many cases, network operators and content providers are at odd when it comes to managing the service. Mobile carriers essentially see these services as non-paying passengers on their transport networks and are either looking at encouraging the offloading of this traffic or to at the very least limit the space that they occupy, particularly in congested areas.

Content providers are predominantly designing services for the internet. It just happens that some of its delivery (increasingly) occurs on mobile devices in cellular networks. The technology and economics of their service is based on the internet model, where bandwidth is plentiful and they are already paying for reach (CDNs) and access (transit and peering). Paying wireless carriers for essentially the same services was  a no-starter until a significant part of their customer based started accessing their services wirelessly on smartphones and tablets. As multiscreen and mobile becomes an important use case, content providers are downloading a streaming player into your devices when you start playing web video on your browser or are enjoining you to use their apps. These are defensive moves aimed at extending the control of the user experience. The reality today is that there are too many players with diverging controlling interests in the delivery of mobile video to make it a good experience. Soon, one will hope, the actors will recognise that no one can control the mobile delivery service end-to-end, forcing cooperation. We are starting to see signs of this with announcements such as Vodafone UK and Netflix exclusive partnership.

We are now at the crossroads where the penetration of mobile devices, the ubiquitous access to fixed and mobile broadband have redefined how video is produced and watched, but not yet how it is delivered.

What would be the attributes of a Video Delivery Network?

Well, ideally it would be designed for both mobile and fixed IP delivery. If we look first at the services it will enable and the business models it is likely to foster, such a network will need to be able to accommodate both live linear video, as well as on demand streaming. It will have to be designed to unlock advertising in a contextually relevant manner and provide frictionless compensation and service level agreement (SLA) management between the actors. Furthermore, models such as pay per use, duration passes, service vouchers, gift cards and sponsored usage will also have to be built in. The corollary from these assumptions is that, in essence, a collaborative service management method is necessary between consumers, announcers, networks and content providers.

What would this network look like, from a technology standpoint?

We have some examples today of partial implementation of these services, in a disjointed, vertical manner. Netflix has transitioned from using commercial CDNs to implementing their Open Connect network. Google Global Cache is extending the content provider’s reach into carrier networks. If we draw this trend to its logical conclusion, a well managed video network will need to have end-to-end managed quality of experience. The only way to achieve this is to integrate player/app/browser/user experience with Radio Access Network (RAN) congestion management, which itself provides explicit data to the Core network for active traffic management that is policy-managed by a negotiated SLA/QoE between content provider, announcer and network. Effectively, this would force network operators to open APIs for announcers and content providers to control the delivery of the content from a quality/speed standpoint. This is the carrier’s contribution to the bargain. The resulting quality of delivery for premium services will be a negotiation in real-time between the demand (content provider and announcer) and the supply (network conditions) at this point in time, for that service, for this user in a specific location. The quality rating at the end or throughout the session should be used as a metric in the calculation of the transfer price of the service. All this can be arbitrated and managed by SLA as it is the case on the internet today.

For freemium, free to air and advertising based services, privacy and regulatory provisions would warrant that each party involved in the ad targeting would retain the use of the data they collect and provide a geographic / demographic / contextual abstraction layer to determine the ad selection. As a result, carriers will need to fundamentally change the way data is collected and analysed, transitioning from operational to marketing view if they wish to monetize the user segmentation. The ad insertion itself should occur as close to the user as possible to enhance contextual and individual granularity. This requirement implies that for encrypted traffic, encryption as well occurs at the point of ad insertion and not before to enable targeting. Technologically, the delivery method should rely on adaptive bit rate DASH to make best use of the network resources, but the encoding should occur in the carrier’s network, with mezzanine files pre-cached and controlled by the content providers.

That ad insertion, encoding and encryption location has been a moving target in the past years because it is where the control point is from a content provider’s perspective. They have allowed CDNs in the past to perform these tasks because they had no other choice, they will need to allow carriers to perform the same to unlock this jigsaw. This is the content provider’s contribution to the bargain.

Inevitably, announcers will have to create an inventory of ads that are mobile specific, not only targeted at devices but at contexts of mobility. Measured quality, high engagement rate and hyper targeted segmentation should help raise CPM in that market.

At last, at the device and radio level, there is no reason that content that is popular would have to go all the way to the content provider’s origin servers to be delivered. An intelligent video service would be able to detect if the service requested is live and linear and watched by others in the area and switch to a broadcast delivery. If the service is on demand, but the content exists closer to the user’s location that is where it should be served from, being from someone else’s device, a network PVR or a cache in the RAN or the core network. There is where network virtualization will take its full capacity, when virtualized storage and networking function can be pushed down to the device level, peer-to-peer transmission will become possible.

What these trends indicate is that a video delivery network will need to be vertically integrated. The boundaries between devices, radio, core and content provider networks will subside, with automation, programmability and virtualization enabling the efficient delivery and management of highly reliable and profitable video service. These questions and more are reviewed in details in my latest reports "Video Monetization and Optimization 2014" and "SDN - NFV in Wireless Networks".

Originally published in The Mobile Network in September 2014.