Most mobile subscribers sign up for LTE services for the lure of a faster network that allows them to access higher quality content that their devices are capable of handling when they want it and where they want it, and to enhance their quality of experience (QoE). LTE provides mobile operators with a low latency, high bandwidth network that is significantly superior to 3G networks, which encourage LTE subscribers to consume more data than the average 3G customer. Ironically, it’s because of this increased use of data that LTE QoE is impacted due to network congestion.
Now Is The Time To Optimize
To reduce congestion-driven problems, operators must accept the fact that intelligent network optimization solutions play a critical role in running an efficient and quality LTE network. Many operators have concluded that mobile video streaming is a major contributor to network traffic. This is verified by Cisco’s Visual Networking Index Global Mobile Data Traffic Forecast, which indicates that mobile video makes up 51 percent of all traffic in 2012, and that two-thirds of the world’s mobile data traffic will be video by 2017. Managing video on a network is one of the main issues that operators need to address in order to continue delivering quality services, better subscriber experiences, and realize maintained revenue. To further underscore this point, Strategy Analytics’ Susan Welsh de Grimaldo stated that more mobile video consumption doesn’t necessarily mean greater spend per user. In fact, she predicted that as mobile video consumption soars, ARPU will decrease. This is why it’s vital that operators work at reducing their cost base by addressing this issue with intelligent video optimization.
Faster LTE networks mean faster, more efficient access to mobile broadband because LTE allows operators to transfer more bits of information on the same spectrum. But as mobile subscribers recognize how fast they can stream video with LTE, they realize that they can now access content at much higher resolutions on their devices with a good user experience and this leads to them consuming more data bandwidth and data volume. Increased LTE streaming exponentially increases network traffic, and subsequently, adds to network congestion. The problem that operators have to manage isn’t addressing the amount of video sent through the network, but the radio network capacity that is strained by the number and frequency of connections subscribers make to retrieve mobile videos. To cope with this, operators must implement congestion-aware video optimization solutions. These solutions intelligently analyze subscriber traffic usage and monitor independent video flows to detect “congested flows,” and only optimize these particular flows to provide a better user experience. The optimization solution can also be deployed in mobile networks that have probes in the RAN infrastructure. The solutions detect congestion and report it to preemptively prevent congestion from occurring on the network. This detection and reporting optimizes the demand on LTE networks and improves response times for subscribers accessing those videos.
Deriving Optimal Value From a Holistic Optimization Solution
In the past, operators engaged with separate vendors to do caching, encoding and decoding, pinching and throttling, and service delivery. In today’s evolved networks, operators must consolidate and implement an end-to-end optimization solution from a single vendor for optimal reliability, performance, and actionable insights. It is when operators have a holistic view of subscriber and network activity that they can efficiently implement a productive optimization solution. Additionally, holistic optimization solutions need to address encoding quality, playback nature, and device-specific delivery.
Encoding quality refers to the way in which the original content has been reformatted for mobile delivery. It takes into account the content type, size of the content, and the device capabilities to deliver the most optimal quality to the device at the least CPU cost to ensure longer battery life for the mobile device. By delivering a mobile video that is optimized for the network instead of sending the consumer the best possible version of that video available from the content provider, operators become “context-aware” and pair the appropriate video with current network resources to provide the best QoE.
Playback nature refers to the stuttering and buffering that occurs with inadequate network download speeds and congested networks. Successful optimization solutions will deliver smooth playback by using context-aware and congestion-aware compression to deliver the video stream at the best possible quality that does not lead to stuttering and buffering.
Finally, optimization solutions must account for the type of device a subscriber is using and the type of content he or she is demanding. By being aware of the device type and content demanded, operators can send only the bandwidth necessary for both, reducing what’s required to send the “best video in this particular context” as opposed to the “best video.” Efficiently addressing these video optimization considerations empowers operators to get the most out of their LTE optimization efforts. It underscores that an operator has a flexible platform that enables dynamic application of optimization practices to best suit the ever-changing needs of the network, instead of a quick-fix for network problems as they arise. Ultimately, this leads to reduced network traffic during peak hours and improved subscriber QoE.
Tackling Congested “Flows” and Congestion “Hotspots”
Optimizing all video traffic at all times is unnecessary and expensive since all-inclusive video optimization is a CPU-intensive function. Congestion-aware optimization that occurs automatically when networks reach certain congestion thresholds can potentially reduce video stuttering and stalling to improve mobile video delivery without the need for a huge server farm. Applying just the right amount of optimization to congestion hotspots, exactly when it’s needed, based on current network conditions, device capabilities, characteristics of the mobile content, operator policies, and customer price plans, leads to CAPEX savings and enhances the customer QoE. The rest of the network is allowed to operate normally, which significantly reduces the cost of deploying optimization solutions and provides the best possible QoE to the users at all times.
Video optimization solutions can also provide LTE networks with an objective measure of the video quality on the network, as they can compare the original and the optimized video quality. When operators can compare video quality from optimized traffic with that from non-optimized traffic, they can draw more intelligent conclusions about their network capabilities. With a better grasp on their network traffic behavior, operators can take advantage of additional revenue opportunities – such as service offerings tailored for heavy video users – made available by the increase in mobile data traffic.
Congestion-aware optimization refers to more than just the amount of LTE network traffic in any given instance. It refers to the type of video content, the devices in use, and the ways in which optimization occurs. Optimization benefits two sides of the wireless network spectrum of operation – service providers and customers. Optimization allows providers to minimize costs associated with network and infrastructure upgrades, while still providing high quality services to subscribers – subscribers who demand more and more mobile video data. In an already-crowded LTE economy, congestion-aware mobile video optimization provides operators with an approach to monitor and effectively utilize their network resources while maintaining customer satisfaction and profitability.
Leveraging Optimization to Truly Harness the Power of 4G Networks
4G provides many benefits that legacy networks lack. Some network architects believe that there is no immediate need to optimize LTE networks, or that they can cover all their bases with new network technologies, such as self-organizing network solutions. This is a misconception because the rate of growth of mobile traffic on the network outpaces the increase in available bandwidth in the network and thus, a robust optimization solution is still critical in helping operators improve QoE while saving large amounts of OPEX and CAPEX, by delaying the need for additional network investments. For example, messaging and voice revenues are beginning to stagnate, which positions data as the primary source for operator revenues and subsequent expenses to evolve data services. Even a well provisioned network requires optimization as part of its long-term strategy to obviate data bottlenecks. Just like 3G, LTE is radio-based, so regardless of how much increased bandwidth LTE provides, it’s still a finite amount, and network congestion will continue to be an issue. Consumers will continue to vie for limited network resources during peak hours, and optimization is necessary to address this. Also, consumers expect all services on LTE networks to be superior and flawless compared to services on legacy networks. A dynamic optimization solution is an essential insurance against subscriber churn because network hiccups, no matter how minor, will be noticed and have the potential to drive subscribers to competing service providers.
In today’s data-centric and unforgiving consumer economy, operators cannot afford to jeopardize QoE in order to reduce data consumption and infrastructure expenses. QoE is the utmost priority in LTE networks, where quality is not just demanded, but expected. LTE network optimization solutions are imperative in ensuring that neither operators nor subscribers have to compromise.
This blog was published by Mobile Marketer on July 15 Click Here