Mobile Video Optimization: Market Evolution and Operator Implications

The popularity of mobile video is evident all around us – in people watching YouTube, Netflix and other content on their smartphones and tablets and communicating via real-time, two-way video. This phenomenon is booming as a result of broad support across the mobile ecosystem, including among devices, networks and services. It shows no signs of slowing, and it has crucial implications for how mobile operators direct their infrastructure investments and strategies.

In this paper, we examine some of the technologies and approaches important to operators as they address the challenges attending mobile video growth – as well as some key considerations to help navigate expected future trends.

The State of Video in the Mobile Network

The growth of mobile video has multiple drivers in a variety of areas. An understanding of these drivers can help illuminate both its history and current trajectory.

Ecosystem Support Drivers

The rise of mobile video adoption has been fueled by many sources – perhaps none more dramatically than the spread of smartphones, whose screens unleashed a new wave of video consumption and whose cameras supplied torrential volumes of video content. Mobile video use continues to expand with the spread of tablets, whose larger screen size improves the video-watching experience and encourages new forms of consumption, including long-form content such as movies. Research has indicated that tablet users are nearly three times more likely to watch video on their device than smartphone users, with 10% of tablet users viewing video content almost daily on their device.1 And although the runaway growth of tablet sales has tapered off somewhat in recent years, growth is now shifting to phablets, whose use is even more concentrated on entertainment.2

Video content owners are eager to fan these flames, aware that adapting their services for mobile devices not only expands their audience, scaling up monetization, but makes their services more tightly woven into users’ lives and therefore indispensable. Some, including Netflix, have created WiFi-only options for their mobile video services to circumvent the conflict between users’ video appetites and the data rates charged by most mobile operators.

The spread of LTE networks – and the greater bandwidth they offer – are another contributing factor in the spread of mobile video, encouraging greater and freer mobile video consumption. That continues as operators deploy LTE-Advanced, with carrier aggregation and other technologies that boost capacity to enable higher levels of video traffic consumption. But the persistent vitality of WiFi offloading reminds us that mobile operators have not adequately evolved their networks to keep pace with mobile video demand.

Mobile Video Traffic Growth

With support from across the mobile ecosystem, mobile video’s growth is well documented. In 2014, video accounted for about 45% of all mobile traffic, according to Ericsson. By 2020, the vendor forecasts that figure to reach 60%. That assumes a traffic growth rate of about 55% per year.3

Various operators have echoed these sentiments. AT&T CEO Randall Stephenson said recently that video makes up half of the company’s mobile traffic, adding that the company’s merger with satellite video provider DirecTV, “Is more about mobile video than home video.”4

What’s more, this video traffic isn’t being distributed uniformly across the network. A large portion of video traffic is consumed by a disproportionately small group of “heavy users.” A heavy user is likely to watch an hour of video per day – 20 times more than an average user, according to Ericsson. In fact, this discrepancy is even more dramatic than it is for overall mobile data, because heavy data users are particular consumers of video.

One implication: operators shouldn’t think of mobile video as just another mode of mobile data. Instead, they should target it specifically to address one of the biggest burdens on their network.

Operator Dilemma

Faced with the prospect of a deluge of video traffic hitting their networks, mobile operators are evaluating ways to accommodate these trends and optimize their networks to deliver video in ways that meets user expectations for quality of experience (QoE).

For many operators, part of the response has been increased radio access network (RAN) investment: densifying networks with additional base stations, building out small cell strategies, using carrier aggregation and other tools to increase spectral efficiency. Outside the RAN, these moves are often supplemented by using traditional content delivery networks (CDN) for mobile and fixed use cases.

Operators are also looking for ways to make use of additional spectrum to provide more network capacity. Options there include typically expensive government spectrum auctions and the ongoing reliance on WiFi offload, which can act as a sort of band-aid on mobile video growth, conserving network capacity but circumventing the mobile data services revenue model in the process.

Some operators are also exploring video-centric solutions such as eMBMS, also called LTE Broadcast. This technology allots network bandwidth to transmit data (including video or audio streams) to

multiple users simultaneously. Due to the efficiency of multicasting, operators have tested its value particularly in sports stadium applications, where a high concentration of users are likely to be interested in the same content at the same time. However, widespread deployment of LTE Broadcast services haven’t materialized (perhaps due partly to a lack of available device support), and some operators and mobile access equipment vendors have questioned the business case surrounding this technology.

Overall, while increasing the spectral efficiency of mobile networks can help accommodate video traffic, the airwaves aren’t the only bottleneck facing video, whose performance can be impacted by everything from router port capacities to server-induced latencies. A comprehensive approach is needed.

The Long Journey of Mobile Video Optimization

Operators concerns around mobile video traffic growth didn’t appear overnight. Video traffic has been growing in fixed networks for years, forcing operators to be aware of it and consider the best way to address it. Its migration to mobile networks posed even more daunting challenges, because – particularly where LTE is not fully rolled out – mobile access networks have historically enjoyed less bandwidth than fixed networks. Even where LTE is available, spectrum is a finite resource, meaning that the impact of any dominant traffic source needs to be carefully considered and planned for.

R&D + M&A Investment

Toward the end of the last decade, as LTE network rollouts ushered in a greater level mobile video consumption (but plenty of 3G infrastructure remained), interest in mobile video optimization swelled, and so did investment in this area. As operators began to face the reality of a mobile video onslaught, vendors rushed in to solve their pain points.

Startup vendors emerged with new solutions. Larger, more established players in related areas such as CDN and operations support systems (OSS) made acquisitions. CDN giant Akamai acquired Verivue Networks, Citrix bought Bytemobile, and Allot Communications purchased Oversi Networks and Ortiva Wireless – all in 2012. Opera Software acquired Skyfire the following year.

Remedy Options

Where vendors rushed in to “optimize” in response to the mobile video problem, operators examined a variety of options for adapting their networks to handle video more efficiently. These options include:

 Transcoding. The conversion from one encoding type to another can reduce file sizes, consuming less bandwidth.  Pacing. Rate-limiting some traffic to achieve “just in time” delivery avoids wasting network resources with content that the user has stopped watching.  Policy. Using policy platforms to distinguish between applications with different QoE needs and subscribers of varying value is another way to take a more discerning approach to using finite network resources.  Transmission Control Protocol (TCP) optimization. TCP, used in the exchange of packet-based data streams in Internet Protocol (IP) networks, wasn’t originally designed for video. A long list of fixes for its flaws included compressing data, eliminating redundant data and traffic-shaping to protect the quality of specific applications.  Adaptive Bit Rate (ABR). Automated adjustment of the bit rate into which a given video stream is encoded helps prevent network congestion from forcing buffering pauses and allows greater QoE when networks aren’t congested.  Caching. Temporarily storing popular content closer to end users shortens the delivery path between content and user, thus conserving bandwidth further upstream in the network.

Buzz Fades

Over time, investment in video and mobile-video optimization gave way to broader investment in optimizing the RAN: Cisco acquired Intucell, JDSU acquired Arieso, and Amdocs acquired Actix and Celcite – all in 2013.

Amid this broader focus on RAN optimization, “mobile video optimization” as a buzzword became less pervasive. However, given the aforementioned distinctions between mobile video and other applications, mobile video optimization will only become more important to operators.

Mobile Video Optimization: The Caching Use Case

As the industry takes a broad, diverse view of optimization, it’s worth revisiting the topic of caching in particular to understand the role it plays today and in the future amid these various technologies. After all:

 Caching is one of the most long-lived and comprehensive technology approaches to these challenges, spanning fixed and mobile networks.  Caching is typically the first response operators consider to video traffic growth challenges in particular, even when employing a broad mix of remedies.  Far from fading in its maturity, caching is only growing more relevant in new RAN optimization innovations, such as the rise of mobile edge computing (MEC).

With that in mind, we take an up-to-date look at current the role of caching in the context of today’s – and tomorrow’s – mobile video growth challenges.

Upstream, Downstream

In the context of all the innovations taking place, the essential premise of caching has only become more valid. Storing popular content closer to the end user creates a shorter path between content and user. This shorter path offers benefits both upstream and downstream of the cached content.

 Upstream of the cached content, it can reduce network bandwidth consumption, saving operators’ costs.  Downstream of the cached content, it can reduce latency, improving end users’ QoE.

How do these benefits add up?

The upstream network bandwidth savings will depend on the particular network, user behavior and solution being employed. In general, major operators and infrastructure vendors have estimated that content caching can reduce backhaul capacity requirements by as much as 35%.5

The downstream savings are harder to quantify because they don’t come from simple transport bandwidth savings. Instead, they relate to how differences in users’ QoE impact customer satisfaction, subscriber churn and average revenue per user. How many milliseconds of latency does it take to lose a customer? One study conducted by Akamai illustrated that more than half of mobile video viewers will have abandoned a video after 30 seconds of delay. The same

study showed mobile users are less likely to return to a given site after a failed attempt to watch video there.6

So how does that dissatisfaction translate into churn? Again, it’s not easy to quantify. But Ericsson has conducted case studies showing a correlation between network performance increases and service revenue growth (e.g., network improvements that granted a roughly 2% increase in network accessibility and data completion rate along with a 5% increase in data throughput resulted in a more than 5% increase in total service revenue; for a mid-sized operator like Sprint, a 5% bump in annual revenue would mean an extra $1.75 billion).7

Ericsson’s study was focused on broad-ranging network improvements; in theory, investments that are focused more specifically on mobile video optimization could be less capex-intensive and perhaps yield an ever greater ROI. In addition, it’s important to keep in mind that – depending on each operator’s relationship with content providers – subscriber satisfaction and churn may be far from the only business impacts of an impaired mobile video experience. While these factors underscore the complexity of calculating the economic impact of caching, the above heuristics, applied to each operator’s own specific circumstances and numbers, can help provide the initial framework to begin addressing the matter.

Moreover, these benefits should increase in both directions the closer the content cache is to the user. Thus, as networks evolve, operators often see access-network changes and upgrades as opportunities to add caching capabilities further downstream – to core networks, packet gateways, controllers, etc. Where mobile networks specifically are concerned, that dynamic has led to its logical next step: the mobile base station.

Base Station Caching

As operators and vendors focus increasingly on optimizing mobile access networks, they have explored the possibilities for pushing content caching to the base station – the furthest end point of the mobile network.

From a commercial deployment perspective, caching at the base station is in an early stage. But already industry members are looking at a variety of approaches to it. Some vendors have offered eNodeBs integrated with commodity servers for content caching or application hosting. Still others have looked at the potential benefits of applying the concept to centralized or cloud RAN architectures. In “C-RAN” networks, the baseband processing units (BBUs) and radio end points that make up the base station – which historically were colocated – are separated. Centralizing BBUs (in “centralized RAN” networks) allows them to be pooled for greater scalability. And virtualizing those baseband pools (in “cloud RAN” networks) adds greater flexibility and efficiency.

When content caching is done in a typical LTE base station, or eNodeB, it offers maximization of the above-mentioned upstream and downstream benefits because it occurs at the point in the network closest to users. When content caching is done from a centralized BBU location further upstream, it offers a subtle compromise: While adding a nominal level of latency and consuming slightly more transport bandwidth, the C-RAN cache has a broader view, encompassing a greater volume of subscriber behavior; thus, it is better informed – including about which content is popular in a given area – and it can act more intelligently and perhaps more efficiently.

RAN Vendor Base Station Caching Activity Alcatel-Lucent Has promoted the concepts of “edge clouds” and cloud RAN, but not introduced specific base- station caching solutions. Altiostar Introduced a cloud RAN solution in 2014 (sold directly or via partner Cisco) and partnered with caching specialist Qwilt in 2015. Ericsson Has not introduced a base station caching solution or promoted participation in driving standardization of MEC, which includes caching use cases. Huawei Introduced Service Anchor solution for caching and app hosting at distributed small-cell networks; participates in MEC standardization efforts. Nokia Introduced Liquid Applications for base station caching and app hosting in 2013, later shifting from caching to “throughput guidance” for content delivery optimization; promotes participation in MEC standardization efforts. Samsung Was an early promoter of base station caching (in 2013) but hasn’t kept active in messaging solutions or standardization efforts. Source: Current Analysis

Challenges Forcing Evolution

Although caching is not the only solution proposed to address mobile video optimization, it has remained preeminent in part because changes in technology trends over the years have left many of the technological alternatives to caching faced with significant hurdles. For example:

 Transcoding. Transcoding may have promised operators more independent control of managing content delivery optimization. But with the rise of ABR methods, content owners have become increasingly less comfortable with the idea of operators potentially degrading the quality of their users’ experience for the sake of more holistic network operations.  Pacing. Pacing was better appreciated when short-form YouTube downloads were more frequent than long-form Netflix streams. As long-form video has grown in prevalence and more dynamic/comprehensive ABR technologies have flourished, it’s become less relevant.  Policy. Policy approaches gave operators a particular level of subscriber-specific control that was more directly tied to revenues. But they can be constrained somewhat by network neutrality concerns, and face challenges in trying to apply uniformity to content from a variety of sources despite the differences among various content providers’ methods.

Open Caching

Caching, meanwhile, faces its own challenges as a result of changing technology trends. Chiefly, the increasing use of encryption limits the visibility that caching platforms need to intelligently manage traffic. Operators and equipment vendors have already begun working on ways to address the issue.

Among the new options being explored is the use of proprietary caches specific to major content providers. For example, Google offers Google Global Cache, wherein network operators and Internet service providers can deploy a small number of Google servers within their networks to cache popular Google content, including YouTube. Unlike in a traditional CDN, Google’s own traffic management system directs users to the cache that provides the best experience for each user. And because the cache is Google’s rather than the operator’s, Google isn’t thwarted by its own encryption.

While solutions like this offer some near-term remedy, it’s not clear how viable they are over the long term. For one, if such activities allow major providers such as Google to offer better performance for their content than smaller content owners that can’t afford to insert their own caches everywhere, the practice could provoke net neutrality concerns. Secondly, if each content provider has its own cache in a given location, the system may not scale very efficiently for the operators that have to deploy and run the networks.

To address all these concerns, industry members have been developing a concept called “open caching,” which is modeled around seven key attributes:

Open

Supporting Universal QoE

Open Caching Software- Resource- Based Sharing

Conserving Network Fair Capacity

Source: Current Analysis

Attributes like openness, fairness and universality address net neutrality concerns, while resource- sharing and the conservation of network capacity address scalability/efficiency concerns. Beyond that, a focus on QoE illustrates a scope of vision in this work that extends beyond the network to the user.

Streaming Video Alliance

Streaming Video Alliance Founding Members Network Infrastructure Alcatel-Lucent Providers Cisco Limelight Networks Qwilt Wowza Media Systems Service Providers Charter Communications Epix Korea Telecom Level 3 Communications Liberty Global Telecom Italia Telstra

Developing open caching technology is the Ustream focus of the Streaming Video Alliance (SVA), an Content Providers Fox Networks Group industry consortium launched in late 2014 by a Major League Baseball diverse mix of network infrastructure providers, Advanced Media service providers and content providers. Yahoo!

Source: Current Analysis

The SVA is working on defining specifications and common approaches that will provide uniformity across these three sections of the mobile video value chain. Specifically, the group has identified three initial areas of focus:

 Open Architectures  Quality of Experience  Interoperability

The group’s work is in an early stage, but the importance of its focus as well as the breadth and credibility of its founding members (with full ranks growing rapidly) make it likely to be highly influential in shaping the future evolution of caching in fixed and mobile networks.

Mobile Video Optimization: Carrier Strategies & Implications

With all the above in mind, how should operators think about employing mobile video optimization technologies, strategies and investments? A few points are paramount:

The launch of the SVA is another reminder that no single technology presently available adequately addresses the network infrastructure needs related to mobile video today and in the future. That said, it’s clear that not all of the technologies proposed for optimizing video delivery in mobile networks will continue to be effective enough to secure long-term futures in operator networks. It’s also clear that caching remains, and will continue to be, an indispensable tool in these efforts, even as (and especially because) it is adapting to other changes taking place in the network. And as caching moves further downstream in operator networks – e.g., to the base station – the economic benefits impacting the upstream and downstream networks could both increase.

Looking at the dramatic changes we’ve seen in recent years, it’s reasonable to expect that change to continue, leading to behaviors and dynamics we can’t predict today. That expectation implies it is crucial for mobile operators to plan for continuing evolution of their mobile networks to adapt to an ever- changing world. These plans need to be focused on not just network efficiency but on the economic value (i.e., capex and opex savings) of that efficiency. They need to be focused not just on QoE but on the economic value (e.g., customer satisfaction, churn, ARPU) of that QoE. The more operators can tie together infrastructure investment and revenue, the better the return on those investments will be and the better positioned they will be to respond to changes and challenges that arise.

Key Considerations

With all the above in mind, the following represents a set of key considerations for mobile operators to bear in mind as they plan their network technology and architecture strategies over the near and long- term:

 How does video QoE impact revenue and revenue opportunities?  Which technologies and methods are most effective in controlling video QoE today?  What trade-offs are inherent in selecting among these technologies, and how do they impact QoE?  Beyond QoE, what other benefits can be derived from mobile video optimization technologies (e.g., reducing network transport capacity needs)?  How can operators quantify the economic impact of both QoE improvements as well as other benefits of mobile video optimization (e.g., reduced capacity needs)?  How do investments in video optimization technologies fit in with other mobile access investments (e.g., LTE-Advanced, C-RAN, heterogeneous networks)?  How can (or should) new optimization investments integrate with legacy infrastructure?

Bearing these considerations in mind will help operators navigate the explosive growth of mobile video that we expect to continue for the foreseeable future. But it will also help address future changes and developments that operators can’t foresee today.

Footnotes:

1. Majority of Tablet Users Watch Video on their Device, 1 in Every 4 Viewers Pay to Watch. ComScore press release, June, 2012. http://ir.comscore.com/releasedetail.cfm?ReleaseID=681569 2. What's Behind the Stall in PC and Tablet Sales. InfoWorld. March, 2015. http://www.infoworld.com/article/2896897/computers/whats-behind-the-stall-in-pc-and- tablet-sales.html 3. Ericsson Mobility Report. June, 2015. http://www.ericsson.com/res/docs/2015/ericsson- mobility-report-june-2015.pdf 4. AT&T CEO: ‘Half of Our Mobile Network Traffic is Video.’ June, 2015. http://fortune.com/2015/06/12/att-randall-stephenson-video-traffic/ 5. Mobile-Edge Computing – Introductory Technical White Paper. MEC Industry Specification Group. September, 2014. https://portal.etsi.org/Portals/0/TBpages/MEC/Docs/Mobile- edge_Computing_-_Introductory_Technical_White_Paper_V1%2018-09-14.pdf 6. Video Stream Quality Impacts Viewer Behavior: Inferring Causality Using Quasi-Experimental Designs. Akamai Technologies. November, 2012. http://people.cs.umass.edu/~ramesh/Site/HOME_files/imc208-krishnan.pdf 7. The Value of Performance. Ericsson. April, 2014. http://www.ericsson.com/res/docs/2014/value-performance-report-screen.pdf