This document discusses Nokia's 3G Application Aware RAN solution which enables real-time application differentiation and prioritization in 3G networks. It analyzes growing mobile data usage and shifting usage patterns. Existing QoS solutions are limited and cannot differentiate applications or account for real-time network conditions. Nokia's solution leverages core network intelligence and RAN awareness to detect applications and enforce policies. Test results showed prioritized applications experienced significantly improved performance even during congestion.

1. 3G Application Aware RAN with
In-bearer optimization
Creating value from application prioritization
Nokia Networks
Nokia Networks white paper
3G Application Aware RAN with In-bearer optimization

2. networks.nokia.comPage 2
Contents
1. Executive summary: Application differentiation creates
added value in mobile broadband
3
2. Mobile data usage will continue to grow rapidly 4
3. Internet usage models shift rapidly 6
4. The evolution of end-to-end data traffic management 7
5. Vision of QoS versus operational practice 7
6. Business models 8
7. Existing approaches for enabling QoS differentiation per
application
8
8. Application Aware RAN enables real application
differentiation in 3G
10
9. Impact of 3G Application Aware RAN QoS on user QoE 11
10. The test cases explored 12
11. Application Aware RAN Test Results 13
11.1 Application Aware RAN priority greatly improves
web browsing performance
13
11.2 Application Aware RAN priority boosts YouTube
video performance
14
11.3 Application Aware RAN priority with in-bearer
application optimization boosts application
multi-tasking
15
12. End-to-end QoE measurement with performance
manager and service quality manager for priority
services
18
13. Find out more 18
14. Abbreviations 19

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1. Executive summary: Application
differentiation creates added value
in mobile broadband
Data usage is growing faster each year and application usage patterns
are changing in unpredictable ways. Operators need better methods to
cope with the dynamic data consumption unleashed by the presence
of smartphones in existing 3G networks. These networks will continue
to carry the majority of data traffic over the coming decade. Even as
additional spectrum, small cell deployments, network features and
optimization techniques help to increase capacity, users expect ever
greater quality in their data sessions. Many subscribers consider data
transmission quality to be as important as network coverage, voice
quality, and price according to recent research1
.
The ability to prioritize application traffic dynamically when needed or
when it adds value for the user and the service provider in a simple but
effective manner creates an opportunity for the operator to move up
the data transport value chain beyond being a pipe provider. Existing
industry solutions for creating application awareness have limitations
that have hindered wide-scale adoption and cannot take advantage of
information from the radio access network (RAN) about cell load and
radio link conditions. In addition, existing solutions are complex to
deploy and cannot react to real-time changes in network and application
behavior.
Nokia Networks’ solution, 3G Application Aware RAN with in-bearer
optimization, leverages existing core network capabilities to inspect
data traffic at the application level while applying policy rules and
enforcement in real-time and end-to-end. Nokia Networks combines
Core Network intelligence with RAN awareness of cell load and radio link
conditions at the bearer level to create a real-time solution for detecting
application data and enforcing policy. Additionally, in-bearer application
optimization takes service prioritization further by extending priority
within the radio access bearer to treat applications with different latency
requirements to assist multi-tasking application users.
The inclusion of the RAN to real-time QoS decisions is the missing link
that gives operators real-time, intelligent control over applications,
breaking all the operational limitations that previously prevented 3G
networks from introducing application prioritization through transport
differentiation. Now operators can create application-specific packages
with personalization and targeted pricing to reflect measurable service
quality.
1. http://networks.nokia.com/news-events/press-room/press-releases/mobile-operators-keep-
your-customers-loyal-by-focusing-on-voice-data-quality-1gbperday

4. networks.nokia.comPage 4
Nokia Networks’ Smart Labs results show that per application-level
detection is very effective and able to provide significant improvements
in data throughput for prioritized services. Smart Labs conducted
a series of tests in which diverse popular applications such as web
browsing, YouTube, Skype, peer-to-peer (P2P) and file download
operated on off-the-shelf Android devices while the cell load varied
from no congestion to high congestion. The prioritized application and
user experienced the following benefits:
• HTTP web browsing data throughput increased 1.65 times when
prioritized in medium-loaded systems and 2.9 times in highly-
loaded systems, compared with testing on a best effort basis in a
congested cell.
• Response times for web services were improved.
• YouTube video data throughput increased 1.93 times when prioritized
in medium-loaded systems and 2.7 times in highly-loaded systems
compared with best effort carriage in a congested cell.
• YouTube video stream re-buffering was reduced or eliminated with
faster stream setup.
• P2P traffic scheduling was more flexible.
Building on the capabilities of Application Aware RAN an additional
feature for in-bearer application optimization enables service
prioritization for multi-tasking application users within a radio access
bearer for latency sensitive applications to be prioritized ahead of
non-latency sensitive ones without changing the QoS Profile.
Prioritized applications were found to experience the following benefits
when tested on a cell with load versus when QoS was inactive:
• FTP+HTTP Multi-tasking application throughput improved by 8 times
• Web page download times were approximately 8 times faster.
• FTP+YouTube Multi-tasking application throughput were 3 times faster
• YouTube video streams did not suffer from re-buffering and buffering
times were reduced from 52 seconds to 6 seconds.
Operators now have an effective system to offer per application priority
at the subscriber level. It is operationally deployable, backwards-
compatible to all 3G devices, and provides added, monetizable value for
the priority delivery of data services.
2. Mobile data usage will continue to
grow rapidly
The smartphone is driving continual increases of data consumption by
subscribers on mobile networks with a five-fold increase in usage to 4 GB

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per month by 2019 from .8 GB per month in 20142
. Usage of web, video,
audio and file sharing continues to rise due to a confluence of widespread
availability of 3G networks, continued speed improvements from HSPA,
and increases in smartphone penetration and device capabilities.
In 2013 mobile networks carried for the first time more than one
exabyte (1 Billion Gigabytes) and the Cisco Visual Networking Index (VNI)
projects that mobile networks will carry 2.5 exabytes of data per month
and further predicts data traffic to exceed 24 exabytes per month by
2019 (Figure 1).
Mobile video which earlier in the decade first became the largest single
traffic type on mobile networks continues to dominate traffic and is
expected to grow to three-quarters of all mobile data traffic by 2019.
Demand for mobile data is closely correlated to the evolution of
device and screen technologies, which are among the areas of the
Information and Communication Technology (ICT) industry that are
evolving the fastest. In 2007 the first iPhone®
was introduced with a
screen resolution of 320 x 480 pixels which in seven years increased
by 13.5 times to a display containing 1920 x 1080 pixels in the iPhone
6 Plus which users are filling with content at two times the data usage
of the “smaller” iPhone 63
. Ultimately, only the human eye will limit the
amount of digital content that will be consumed by a mobile device. In
addition to consuming content, ubiquitous integrated cameras with high
resolution and frame rate are producing exabytes of digital content to
be distributed via networks.
Clearly, mobile networks are facing a growing possibility of congestion
during peak usage hours, despite investments in additional base
stations, advanced RF features, and other capacity improvements.
Fig. 1. Cisco VNI global mobile
data traffic growth.
Exabytes/month
Mobile Data Traffic Growth
2019 mobile networks predicted to carry
more than 24 Exabytes per month
22001144 22001155 22001166 22001177 22001188 22001199
0
24
12
2. http://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html
3. http://www.citrix.com/content/dam/citrix/en_us/documents/products-solutions/citrix-mobile-analytics-report-february-2015.pdf

6. networks.nokia.comPage 6
3. Internet usage models shift rapidly
While mobile internet network traffic continues to rise, there is a
noticeable change in usage patterns. Video is embedding itself into
more application categories and the types of application which
subscribers use are changing to include new categories and at different
times of the day.
For example according to a Citrix Mobile Analytics Report4
, a new small
but growing category, mobile dating, is used the most at 6 PM, while
healthcare / fitness applications which grew from 39% to 78% of
subscribers in two years have peak usage between 5-7 PM.
The usage of any content type is dynamic and in context of the
application being used. Most YouTube users watch videos for less
than 5 minutes at a time while on NetFlix a majority of users watch for
more than 5 mins4
. Embedding of video in applications has increased
to include social media applications like Facebook to messaging
applications like Snapchat / Instagram, and into new categories like
mobile gaming where two years ago none of top five applications
contained video to all five today4
. Users and their devices are multi-
tasking far more than before triggering multiple simultaneous data
sessions with different QoS requirements.
Interestingly, according Nokia’s own Acquisition and Retention Study5
report 41% of customers expect excellent network quality even if it
costs more. 3G networks need to be able to support rapid changes in
usage which adjust to work at the speed of the user.
Fig. 2. Use of different smartphone
applications by users.
PhotosVideoMusic Games ShoppingProductivity Storage
… … …
4. http://www.citrix.com/content/dam/citrix/en_us/documents/products-solutions/citrix-mobile-analytics-report-february-2015.pdf
5. http://networks.nokia.com/news-events/press-room/press-releases/network-and-service-quality-keeps-customers-loyal-nokia-
retention-study-shows

7. networks.nokia.comPage 7
Fig. 3. Current business models treat traffic equally under given conditions.
IMEI
Smartphone
Tablet
Thermostat
IMSI
Gold
Silver
Bronze
Volume
Monthly
Daily
Time
9AM to 11 AM
4PM to 5PM
Location
Home Zone
Access
2G, 3G, LTE
4. The evolution of end-to-end data
traffic management
How can operators and their customers adjust to the impact of data hungry
applications, especially since many data plans have pre-defined usage limits
and the popularity of different applications keeps changing? Traditional
traffic management and billing models (Figure 3) are inflexible on a per
application basis.
Most types of data traffic are treated equally under a given set of conditions,
such as device type (IMEI), subscriber level identity (IMSI), access type (2G,
3G, LTE), time of day, data volume and location.
5. Vision of QoS versus operational
practice
When the wireless industry standardized Quality of Service (QoS)
differentiation mechanisms in the 3GPP (Third Generation Partnership
Project) more than a decade ago, Access Point Names (APN) with separate
primary bearer (Packet Data Protocol Context, of “PDP Context”) were
created to support QoS for data connections. However, it proved
impractical to manage multiple APNs per device across the network as the
number of applications and connections proliferated. This challenge was
compounded by the complexity of 3G QoS and associated device support,
which meant that the QoS mechanism was not used to the fullest extent
possible in operational networks.
The result of treating different traffic types equally or with limited number
of levels owing to the limitations of PDP context-level QoS is a constrained
business model. Single payer models, preferred and paid prioritization can’t
bring as much value and flexibility either to the customer or the service
provider because of the lack per application-level QoS differentiation.
If application-level differentiation can be enabled, then per application-
level management of the Quality of Experience (QoE) is possible, enabling
less important data to be delayed and preferred data to be prioritized.
New application-level pricing models can be offered thanks to transport
being a value added delivery service, rather than a best effort pipe.

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6. Business models
Clever, tiered pricing models and bundles containing mobile broadband data
services are major tools to help operators combat revenue erosion. This will
only become more important going forward as operators explore new models,
such as price differentiation by quality and application.
Ultimately, however, there is an increasing danger that the business of
being a network operator is changing from a retail model to a utility type of
business, with limited room for positive differentiation. Handset vendors and
OTT providers are gaining more traction with consumers and it will be harder
than ever for operators to establish strong customer relationships in the
future as consumers are focused on the latest device and the coolest app.
In most markets, customer loyalty is in decline. Consumers are increasingly
selecting their mobile broadband service provider based on coverage,
performance along with service price and handset offers.
It’s also clear that the number of cooperation agreements between operators,
OTT vendors and other industries will increase significantly, especially in the
areas of content delivery. Delivering services in a differentiated and managed
way opens up additional personalization and monetization opportunities
in partnership with content providers and global content delivery networks
(CDNs) by providing a clear value-add to the partners in the value chain and
ultimately the end users. A good analogy would be a value-adding logistical
service such as a premium postal service offering fast and reliable delivery.
Consider the example of video traffic delivery. The video traffic quality
issue can only be rectified by the network operator. The operator owns and
operates the only portion of the network between video servers and digital
video players that does not carry an explicit Service Level Agreement (SLA). If
operators can ensure a better service quality for specific OTT video streams
and provide SLAs on those streams that include the journey through the
RAN, various parties including consumers might be willing to pay for the value
added transport. Content providers want their end users to receive their
content at a reasonable quality. There are several potential revenue sources
for the operator: the end user paying for “premium” internet TV, the global
CDN, the content aggregator and the content provider paying for an explicit
SLA (in markets which allow various forms of paid prioritization).
7. Existing approaches for enabling QoS
differentiation per application
The industry has created a number of QoS differentiation solutions in an
attempt to solve the need for application differentiation.
These solutions have all seen some level of adoption depending on the
needs of the 3G HSPA network operator but they each have limitations that
have prevented their use on a large scale.

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• Core based application throttling
Application throttling is triggered by deep packet inspection (DPI) based
application detection, subscription, fair use policy, time of day, the user’s
initial cell location and a prediction of cell peak hours. Application IP flow
throttling is enforced within the core network.
Limitations: The system is not aware of cell-level loading in real time. To
add cell load awareness requires complex system integration (OSS, Policy,
GW, DPI) and will be inefficient and inaccurate.
• Network-requested PDP context QoS modification
Trigger for modification of the PDP context bearer is same as in core
based application throttling.
Limitations: Modifications impact the whole bearer so all applications are
affected by any change. Frequent modifications cause high signaling load
in all network elements (GGSN, SGSN, RNC, NodeB).
• Dedicated access point name (APN) per application
Primary PDP context level QoS differentiation can be provided by an
application-specific APN (device OS/application support), which is limited
to certain services, domains and operator or partner content. Normally
only operator service specific APNs are supported (e.g. MMS, IMS). Thus
application specific APNs are not really an option.
Limitations: There is no true application awareness within the PDP context
to determine which applications benefit. Typical usage in networks is
limited to specific services with policy rules. APN configuration information
requires the operator to push terminal configuration parameters to
the device and provide support from device software. It’s operationally
complex to implement, manage and maintain.
• Network-requested secondary PDP context and dynamic application
mapping
Selected applications are detected by DPI and secondary PDP context is
established for application specific traffic. Traffic flow templates in core
and user equipment (UE) map application IP flows to secondary PDP
context in order to provide differential QoS.
Limitations: This is only supported by LTE terminals and not currently by
3G (or 2G) terminals. It creates challenges in handling a mass of short-lived
uplink flows (such as P2P demotion). It creates delay because of the need
to activate radio resources when the first data arrives at the dedicated
bearer.
Each of these existing solutions solves some problems, but none of them
fully address radio access, which is the best real-time enforcement point for
per application QoS differentiation. Dynamic radio access scheduling must be
combined with the core network’s control and logic enforcement in order to
react dynamically to network conditions and user application usage.

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8. Application Aware RAN enables real
application differentiation in 3G
The wireless industry needs an end to end, per application level QoS
solution, but has not been able to implement a comprehensive system
for 3G QoS.
Nokia Networks has innovated with the creation of 3G Application Aware
RAN with in-bearer optimization an end to end QoS solution which
works with all existing HSPA-capable devices allowing operators and
their customers to prioritize important and specific data traffic flexibly,
without operational complexity and past limitations.
Nokia Networks’ Application Aware RAN is a dynamic, real-time solution
which (Figure 4) connects all the needed subsystems into one end
to end chain for QoS differentiation down to a per subscriber per
application level even within the same radio access bearer (RAB) which
opens up the possibility to prioritize multi-tasking subscribers who may
be using foreground and background applications simultaneously.
The solution works within the current 3GPP standards and network
elements by using QoS policies from the Subscriber Profile Repository
(SPR) which are used by the policy charging and rules function (PCRF)
to program the policy control enforcement function (PCEF) to mark IP
packets which are detected using deep packet inspection (DPI). These
markings are used by the Radio Network Controller (RNC) for bearer
priority and by the BTS to dynamically change bearer priorities in real-
time using Nokia’s advanced radio scheduling algorithms. Fast radio
scheduler reactions react to changing cell loads, radio conditions and
policy needs.
3G BTS RNC
Define application and subscriber specific
QoS profiles
Internet
Charging
PCEF+DPI
DPI: monitor and detect application use
while marking applications according to policies
SGSN PCRFSPR
Supported by
all devices
Real-time QoS enforcement and cell load aware
Best QoE and efficiency of the most critical
system resources
OSS
Fig. 4. Nokia Application Aware RAN end-to-end system approach.

11. networks.nokia.comPage 11
Test UE
Other UE’s for Load
Application Aware RAN User
Best Effort or lower priority User
RNC
SGSN
PCEF
PCRF
3G
BTS
Fig. 5. Lab setup for testing 3G Application Aware RAN and Application Aware RAN with in-bearer
application optimization
9. Impact of 3G Application Aware
RAN QoS on user QoE
In order to benchmark the efficacy of the Nokia 3G Application Aware
RAN solution, a series of lab tests were conducted in the Nokia
Networks’ Smart Labs using a 3G HSPA network with commercially
available HSPA Android-based smartphones. Note that Nokia’s
Application Aware RAN solution is network-based, dynamic, cell-load
aware and terminal-independent, so it supports all HSPA devices.
Depending on the test, five different common smartphone activities
were tested, including web browsing, file download, YouTube, P2P
torrent, email and Skype with and without multi-tasking.
The test setup is shown in Figure 5. Note that real-world results may
vary from lab

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10. The test cases explored
The test labs looked at five types of applications commonly used in an
HSPA network under varying levels of load. They tested the impact of per
application priority setting versus no priority as a best effort application.
Additional scenarios explored real-world user behavior, which typically
involves using multiple applications simultaneously on one device with
priority-setting as the network load varies.
For basic Application Aware RAN, Nokia Smart labs tested the selective
prioritization of applications on a device in preference to other
applications on the same device. Once the base test cases were
completed then Application Aware RAN with in-bearer application
optimization was tested to find the performance when users multi-task
and use FTP+HTTP or FTP+YouTube which varying priority needs.
Table 1. Description of test cases to verify the impact of Application Aware RAN.
Test Scenario Test Description
Unloaded system Single application (web browsing,
file download, YouTube, P2P torrent
and Skype) with PRIORITY
Medium cell load Single application with
NO PRIORITY (best effort)
Single application
with priority
Application Aware RAN
High cell load Single application with
NO PRIORITY (best effort)
Single application
with priority
Application Aware RAN
in-bearer optimization
FTP+HTTP
FTP+HTTP
on single RAB with cell load
NO PRIORTY (best effort)
Multi-tasking
with varying
application priority
Combination of Application
Aware RAN and in-bearer
application optimization
in-bearer optimization
FTP+YouTube
FTP+YouTube (HD video 720p)
on single RAB with cell load
NO PRIORITY (best effort)
Multi-tasking
with varying
application priority
Combination of Application
Aware RAN and in-bearer
application optimization

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11. Application Aware RAN Test Results
11.1 Application Aware RAN priority greatly
improves web browsing performance
Nokia Networks’ Smart Labs testing of prioritized HTTP web browsing
with Application Aware RAN shows significant performance improvements
resulting in higher user satisfaction for prioritized versus non-prioritized
(best effort) sessions during periods of congestion.
Test results showed (Figure 6) remarkably improved service quality for a
user with web browsing priority under different load conditions:
• Under medium cell load with prioritization, HTTP throughput increases
from 4.1 Mbps to 6.8 Mbps or 1.65 times compared with tests with no
priority as a best effort application.
• Under high cell load with prioritization, HTTP throughput increases
from 1 Mbps to 2.9 Mbps or 2.9 times compared with tests with no
priority as a best effort application.
• All tests show a general improvement in response times for web
services.
10
2
4
6
8
12
0
HTTP
Priority
No Load
HTTP
Priority
Medium
Load
HTTP Web Browsing Results
No HTTP
Priority
Medium
Load
No HTTP
Priority
High Load
HTTP
Priority
High Load
DataRate(Mbps)
Application Aware RAN Increases HTTP
Throughput in Cell Congestion
User with HTTP priority maintains higher data rates
Fig. 6. HTTP browsing results, prioritized vs.
non-prioritized.

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11.2 Application Aware RAN priority boosts
YouTube video performance
If Application Aware RAN prioritization can improve web browsing for
selected users during periods of congestion, what effect can it have
on demanding video sessions? Nokia Networks’ Smart Labs applied
application priority to a user streaming a 30 second, 720p YouTube
video clip to an off-the-shelf Android device. Cell-level congestion
conditions were varied from no load to high load using additional
devices. A performance comparison of YouTube sessions with
application priority and with no application priority (normal best effort
data) was conducted.
Application Aware RAN created significant performance improvements
in data throughput at times of congestion for a user with a prioritized
YouTube service (Figure 7):
• Under medium cell load with prioritization, throughput increases from
3.2 Mbps to 6.2 Mbps or 1.93 times compared with tests with no
priority as a best effort application.
• Under high cell load with prioritization, throughput increases from
1 Mbps to 2.7 Mbps or 2.7 times compared with tests with no priority
as a best effort application.
Fig. 7. YouTube video session results,
prioritized vs. non-prioritized.
5
1
2
3
4
6
7
8
0
Video
Priority
No Load
Video
Priority
Medium
Load
YouTube Video Streaming Results
No Video
Priority
Medium
Load
No Video
Priority
High Load
Video
Priority
High Load
DataRate(Mbps)
Application Aware RAN Boosting video streaming performance.
User with YouTube Priority has higher data rates for video sessions during
congestion with faster server access and less buffering.

15. networks.nokia.comPage 15
• The user with a prioritized YouTube service also experiences faster
server access, making it quicker to set up a video stream. More
importantly, when the cell experiences high load, video buffering
times are substantially decreased.
• YouTube data is successfully detected and prioritized, while other
application data continues as best effort traffic.
11.3 Application Aware RAN priority with
in-bearer application optimization boosts
application multi-tasking
If Application Aware RAN prioritization alone can improve performance
what is the impact of enabling the in-bearer application optimization
feature along with Application Aware RAN?
In-bearer application optimization goes to the next level of Application
Aware RAN by prioritizing applications or services within an access
bearer where different application types can be scheduled according to
their latency sensitivity. This reflects real-world smartphone scenarios
with multiple applications running in parallel.
Nokia Networks’ Smart Labs applied both Application Aware RAN and
in-bearer application optimization to observe the affect of both features
running together when a smartphone is multi-tasking by operating two
different types of applications simultaneously.
For the tests, off the shelf Android devices were used on a loaded cell
with the UE of interest being tested multi-tasking first FTP+ HTTP and
then FTP+YouTube.
Application Aware RAN with in-bearer application optimization created
significant performance improvements in data throughput and
experience for the user and for the preferred application(s) versus if no
QoS policies were active (Figure 8) in loaded conditions:
• With priority for HTTP data traffic multi-tasking with a background
application, application throughput increased from 1.07 to 2.31 Mbps
for FTP and from 0.34 to 2.85 Mbps for HTTP compared to when QoS
was inactive for an 8 times HTTP throughput improvement
• With priority for HTTP data traffic multi-tasking with a background
application, webpages load times were reduced from 141 to 18
seconds as compared to when QoS was inactive for almost a 8x
reduction in time

16. networks.nokia.comPage 16
• With priority for YouTube data traffic multi-tasking with a background
application, throughput Increased from 0.6 to 2.8 Mbps for FTP and
from 0.7 to 2.1 Mbps for YouTube compared to when no QoS was
inactive for a 3 times YouTube throughput improvement
• When YouTube is multi-tasking with a background application, initial
video buffering times were reduced from 52 seconds with no QoS
enabled to 6 seconds when both Application Aware RAN and in-bearer
application optimization were operational and user annoying
re-buffering events dropped from 59 to 0 events
0
1
2
3
UE FTP TP
UE HTTP TP
0.34
2.31
1.07
DataRate(Mbps)
No QoS Application Aware RAN +
In-bearer App Optimization
FTP + HTTP
0
1
2
3
UE FTP TP
UE YouTube TP
2.1
0.7
2.8
0.6
DataRate(Mbps)
No QoS Application Aware RAN +
In-bearer App Optimization
FTP + YouTube
2.85
141 28 18
0
20
40
140
No QoS Application aware RAN Application aware
RAN + In-bearer
App Optimization
Webpagedownloadtime(sec)
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BBaacckkggrroouunndd FFTTPP ++ ffoorreeggrroouunndd BBrroowwssiinngg..
Application Aware RAN with in-bearer application optimization improves both the application
performance vs. no QoS and further increases the throughput and helps delay sensitive applications in
cell load conditions
Application Aware RAN optimization improves user experience by reducing
web page download times

17. networks.nokia.comPage 17
52 9 659 0 0
0
10
50
60
No QoS Application
aware RAN
Application aware
RAN + In-bearer
App Optimization
SSmmaarrttpphhoonnee mmuullttii--ttaasskkiinngg iinn llooaaddeedd cceellll..
BBaacckkggrroouunndd FFTTPP ++ ffoorreeggrroouunndd YYoouuTTuubbee HHDD 772200pp..
YouTube initial
buffering (sec)
Number of YouTube
re-buffering events
Application Aware RAN optimization improves user experience by reducing video
buffering times from click to view vs No QoS and reduces annoying re-buffering
of videos

18. networks.nokia.comPage 18
12. End-to-end QoE measurement
with performance manager and
service quality manager for priority
services
Ensuring service quality for more demanding applications such as
YouTube requires Operational Support Systems (OSS) to let the
operator know that the enabled priority plan is working as expected.
Nokia Networks has designed OSS support to monitor application
performance in management systems, with an overview and drill
down support available for Application Aware RAN. Operators can
monitor high-value applications within the network 24/7, with views
of differentiated application throughput at the cell level and root
cause analysis of service degradation, if it occurs. With the integrated
measurement capability from Nokia Networks’ management service,
operators can see QoE analysis with easy reporting of differential
throughput for users and applications.
13. Find out more
Contact Nokia Networks for more details and the results of the other
Smart Labs tests for Application Aware RAN for 3G and how Nokia can
help you add value from prioritization to your network.
Measure active application
throughput for services for end
user experienced DL throughput
Monitor application performance
using Performance Manager/Service
Quality Manager
Holistic view or drill down service level
View differentiated app throughput
easily - per app priority class cell
Performance Management for precise following of application priority classes
Know what you deliver to meet marketing promises ensure great user experience
Fig. 9. Nokia performance management systems for application monitoring.

19. networks.nokia.comPage 19
14. Abbreviations
2G Second Generation cellular
3G Third Generation cellular
3GPP Third Generation Partnership Project
APN Access point name
BTS Base Transceiver Station
CDN Content delivery networks
DPI Deep packet inspection
NB NodeB
GBR Guaranteed bit rate
GGSN Gateway GPRS support node
GW Gateway
HSPA High-speed packet access
HTTP Hypertext Transfer Protocol
IMSI International Mobile Subscriber Identity
IMEI International Mobile Equipment Identity
LTE Long Term Evolution
NW Network
OTT Over-the-top
OSS Operational support systems
PDP Packet Data Protocol
P2P Peer-to-peer
PCEF Policy Control Enforcement Function
PCRF Policy Charging and Rules Function
QCI QoS Class Indicator
QoS Quality of service
QoE Quality of experience
RAN Radio access network
RNC Radio Network Controller
SGSN Serving GPRS Support Node
SLA Service Level Agreement
UE User equipment

20. networks.nokia.comPage 20
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