System and method for optimization of an over-the-top (OTT) platform

A system to optimize resource utilization in an over-the-top (OTT) platform to improve Quality of Experience of viewing a video at the user devices in a mobile communication network is provided. The system is operably coupled to a sensing unit for receiving a sensory input parameters from the user devices, mobile network operator, and an OTT platform, a sensing unit generates a sensed data using received sensory input data; a processing unit receives and process the generated sensed data to determine changes required in a resolution of video provided to the user devices, if resolution of video is below a threshold value; an acting unit provides instructions to OTT platform to stream video to the user devices over a constant bandwidth or constant resolution to prevent transcoding and transrating and conserver resource utilization at OTT platform.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of the non-provisional patent application titled “System and Method for Optimization of an Over-The-Top (OTT) Platform”, application number 201941003078, filed in the Indian Patent Office on Jan. 25, 2019. The specification of the above referenced patent application is incorporated herein by reference in its entirety.

BACKGROUND

In this modern age of technology, the internet is touching almost every part of our digital life. With connectivity between user devices, for example, electronic devices, mobile phones, etc., becoming ubiquitous, the number of internet applications as well as users has ballooned over the last few years and continues to grow at a phenomenal rate. As any such new-age ecosystems face, there are also challenges with this phenomenal growth. One of the biggest challenges for the internet is the assurance or guarantee of Quality of Experience. Be it un-buffered, high-quality video or ability to provide uninterrupted connectivity for an e-commerce site during a purchase. With the advent of new-age internet applications such as Internet-of-Things, self-driving cars, online video games and so on, the challenge of guaranteeing Quality of Experience has increased. The quality of assurance now moves from just assuring bandwidth to other factors such as latency, assured connectivity, etc.

Moreover, the mobile communication network has limited bandwidth for data transmission; due to which video streaming services over a mobile communication network are affected, for example, a data loss in data transmission. Due to data loss, guaranteed streaming video quality is not obtained at the user devices. Therefore, the study of video streaming over mobile communication network is critical to enhancing the Quality of Experience of viewing a video in the user devices.

Furthermore, the present internet applications and mobile communication networks function independently without understanding the capability of the other. The mobile communication networks are not concerned with the type of applications running on the user devices to optimize the mobile communication networks. Similarly, internet applications are not concerned with the capability of the mobile communication networks used for communications.

Generally, multiple users would like to view a high resolution of a video over a network. However, to view a high resolution video of a video file, a large number of resources are required. Transcoding and/or transrating are performed at an over-the-top (OTT) platform to obtain a required resolution if it is not possible to provide the required resolution to the user devices, as per the user's subscription plan. Therefore, conserving utilization of the resources over a mobile communication network is critical.

Hence, there is a long felt but unresolved need for a method and system to optimize resource utilization at the service provider. Moreover, there is a need for a method and system to improve Quality of Experience of viewing a video in one or more of the end-user devices. There is a need for a method and system to build a bridge between the internet applications and mobile communication networks so as to collect and process the user experience data in real time based on three fundamental principles they are: Sensing the current quality the user is experiencing, Processing the sensed data, that is sent to a management platform in the cloud and action need to be taken to initiate optimization changes to the mobile communication network to improve the Quality of experience for end-user devices. Furthermore, there is a need for a method and system to reduce transcoding and transrating performed at an over-the-top (OTT) platform providing the video and conserve resource utilization at the OTT platform.

SUMMARY OF THE INVENTION

This summary is provided to introduce a selection of concepts in a simplified form that are further disclosed in the detailed description of the invention. This summary is not intended to determine the scope of the claimed subject matter.

The method and system disclosed herein addresses the above-recited need for optimizing resource utilization in an over-the-top (OTT) platform in order to improve the Quality of experience for one or more of the end-user devices. Moreover, the method and system disclosed herein receives the sensory data, and determines the changes required in the sensory data to improve Quality of Experience of viewing a video on the one or more of the end-user devices in a mobile communication network. Furthermore, the method and the system disclosed herein conserve the resources at the service provider. For example, the method and the system disclosed herein prevents transrating and transcoding performed by the service provider, thereby optimizing resource utilization at multiple devices, for example, end-user devices, service providers, etc., connected to the mobile communication network.

The method disclosed herein optimizes resource utilization in an over-the-top (OTT) platform to improve the Quality of Experience of viewing a video in the one or more of the end-user devices connected to the mobile communication network. The system comprises a sensing unit for receiving a plurality of sensory input parameters from one or more of the end-user devices, a mobile network operator, and the OTT platform. The sensory input parameters received from the OTT platform comprise transcoding and transrating performed at OTT platform. The sensing unit generates sensed data using received sensory input parameters. The system further comprises a processing unit for receiving generated sensed data. The processing unit processes the generated sensed data to determine changes required in a resolution of a video provided by the OTT platform to one or more of the end-user devices, if resolution of the video is below a threshold value. The system further comprises an acting unit for providing instructions to the OTT platform to stream the video to the one or more of the end-user devices receiving the video with a resolution below the threshold value. The instructions provided to the OTT platform comprise instructions to transmit video at a constant resolution, where the constant resolution is the threshold value or above the threshold value. The instructions provided to the OTT platform further comprise instructions to transmit the video over a constant bandwidth to each of one or more of the end-user devices. Transmitting the video at one of a constant resolution and a constant bandwidth prevents transcoding and transrating at the OTT platform, thereby conserving resource utilization at the OTT platform.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1exemplary illustrates a system100to optimize resource utilization in an over-the-top (OTT) platform101to improve Quality of Experience of viewing a video in one or more of the end-user devices102in a mobile communication network. As used herein, the term “Quality of experience” is a measure of an experience of a video quality when the end-user device102is viewing a video. The Quality of experience inputs are provided through a plurality of sensory parameters to the sensing unit103, for instance, the type of android application package (APK) running on the end-user devices102, information on radio link strength and quality for example, received signal strength indicator (RSSI) and carrier to interference-plus-noise ratio (CINR) from the Mobiles, traffic data, for instance, video statistics, video contents consumed by the end-user device102and/or their uniform resource locator (URLs), when the user logs on to view and/or consume the content, current bandwidth and/or the quality of the said videos, the times of viewing, whether any buffering occurred, the total number of video bytes consumed, wherein sensory parameters are also referred to a sensory data. Also, as used herein, the term “OTT platform” refers to the content and/or service provider101to the end-user devices102over a network, the OTT platform provides a plurality of sensory parameters to the sensing unit, wherein content includes resources required for performing transcoding and transrating on the video viewed by one or more of the end-user devices, computational resource, for example, CPU utilization, current latency obtained by user devices102. Also, as used herein, the term “mobile communication network” refers to radio access network (RAN), mobile network as defined in 3GPP standards, with Node-Bs/e-Node-Bs, the Evolved Packet Core (EPC) including the Mobility Management Entity (MME), Serving Gateway (S-GW) and Packet Data Network Gateway (P-GW), etc., one of the internet, an intranet, a wireless network, a mobile telecommunication network such as a code division multiple access (CDMA) network, a third generation (3G) mobile communication network, a fourth generation (4G) mobile communication network, a fifth generation (5G) mobile communication network, a long-term evolution (LTE) mobile communication network, etc., or a network formed from any combination of these networks.

The system100disclosed herein comprises the resource optimization platform operably coupled to a mobile communication network; a mobile device also refers to user equipment (UE)102is configured with mobile communication network, wherein the mobile device102, for example, a smartphone, a gaming console, a personal digital assistant (PDA) or tablet, a Laptop, an internet of thing devices like a sensor or an actuator attached to and/or providing information about a vehicle like for instance, a car, or an appliance such as a home appliance or a smart meter, or any other platform configured to access the communication network, etc.

The system100disclosed herein comprises the resource optimization system operably coupled to a sensing unit103, herein the sensing unit103receives a plurality of sensory data from the UE102, for example, traffic data, network inputs, and QoE inputs and from over-the-top (OTT) platform101, for example, resources for performing transcoding and transrating on the video viewed by one or more of the end-user devices102, computational resource, for example, CPU utilization, current latency obtained by one or more of the end-user devices102for the service being given to the end-user102and the sensing unit103also receives a plurality of sensory data from mobile network operator, for example, mobile station international subscriber directory number (MSISDN) of one of an individual end-user devices102and mobile internet protocol (IP) address, wherein mobile network operator provides MSISDN to each of one or more of the end-user devices102to identify a mobile phone number internationally. In the system100disclosed herein, the UE102, over-the-top (OTT) platform101, a mobile network operator, etc. are positioned at different places over the network.

In one embodiment, Quality of experience is a measure of a video quality experienced when the end-user102is viewing a video. The Quality of experience inputs are provided through a plurality of sensory data to the sensing unit103for instance, the type of application (APK) running on end-user devices102or a Software Development Kit (SDK) integrated into another application that is running on a mobile device, a gaming console, a PDA or tablet, a Laptop, either in the foreground or background, sensing the OS (Operating System like for example, Android) running on the end device. An Intelligent Logic that resides in different points in the mobile communication network, for instance, a Network Address Translator (NAT)119Interface, a Dynamic Host Configuration Protocol (DHCP) Interface, an S6a Interface, a 3GPP user authentication, authorization and accounting (AAA) server118, S1-MME113, GTPv2, Gx, Gy, Sp interfaces and/or combinations thereof, that provide the IP address mapping to the MSISDN, information on radio link strength and quality (RSSI and CINR) from the mobiles, traffic data comprises video statistics, video contents consumed by the end-user devices102and/or their URLs, there is a script running on the content server or content delivery network (CDN)110server that gets loaded onto the end-user devices102to collect the required information about when the user is log-in to view and/or consume the content, current bandwidth and/or the quality of the said videos, the times of viewing, whether any buffering occurred, total number of video bytes consumed, vital information about subscriber location like LAC/TAC (Location Area Code/Tracking Area Code), online and offline charging system parameters of each of the Network operators taken from the Gy and/or Gz and Sh interfaces as defined in the 3GPP standards TS 32.296 and TS 32.240, TS 32.251 and TS 32.299, and TS 29.329 including number of bytes consumed per service and the cost, NodeB and/or eNodeB details including the Cell IDs, policy applied, user profile, usage of data, policy updates etc., detected through end-user devices102and resource utilization in the RAN including radio resources and computational resources like CPU utilization detected through NodeB/eNodeB, and the current video quality being given by the service provider101to the end-user devices102.

As exemplarily illustrated inFIG. 1, the system100comprises a storage unit for storing a set of rules and/or instructions and at least one processor108communicatively coupled with the storage unit and operably coupled to the sensing unit103. As used herein, storage unit comprises a rule engine which is communicatively coupled with the mobile network operator. The machine learning algorithms are stored in the rule engine for handling complex network slicing operation. For a complex network slicing operation, the rule engine is configured to change or modify the set of rules by changing the scheduling policies at the radio access networks based on the needs of the users102. As used herein, the machine learning algorithm refers to all learning algorithm, for example, traditional batch mode learning methods such as decision trees, support vector machines, bayesian networks, clustering, ensemble learning algorithms, and Markov Chain Monte Carlo (MCMC) algorithms as well as versions of these algorithms adapted to data stream processing. The machine learning algorithm can operate in supervised, semi-supervised or unsupervised learning mode depending on the amount of labeled training data available. Based on the video quality predictions, the rule engine provides actionable recommendations to the wireless network for addressing QoE issues.

The system100comprises a sensing unit103, a processing unit108, an acting unit109communicatively coupled with the storage unit. The sensing unit103dynamically receives sensory data from the plurality of UE102comprising video attributes, for example video contents consumed by the end-user devices102and/or their URLs, when the user is log-in to view and/or consume the content, current bandwidth and/or the quality of the said videos, the times of viewing, whether any buffering occurred, total number of video bytes consumed etc. Further, the sensing unit103dynamically receives the sensory data from the service provider101, for example, computational resource refers to CPU utilization, etc. The sensing unit103dynamically receives the sensory data from the mobile network operator, for example, mobile IP address, MSISDN of the user equipment102. The processing unit108receives the sensory data from the sensing unit103and the processing unit108is functionally coupled to the acting unit109.

In one embodiment, the processing unit108and the storage unit is configured to cloud-based services implemented in a cloud computing environment, where computing resources are delivered as a service over the network. The system100is a cloud computing based platform implemented as a service for determining resource optimization in OTT platform101.

The system100comprises a first controller104and a second controller106for controlling the data transmission from origin to the destination and is operably coupled to the processing unit108over a network. The first controller104refers to Cloud and/or centralized radio access network (CRAN) and orchestrator, wherein, CRAN connects UE102to the cellular antenna, process the data and transmit it to the mobile communication network, herein orchestrator refers to a software-defined networking (SDN) Orchestration, wherein SDN is a process of automatically programming behavior of the network, so that the network smoothly coordinates to support applications and services. The first controller104transmits processed data to off-loader module105. The processed data from the off-loader module105are transmitted to the UE102as per user capabilities of utilizing video resources over the network. Another key service for distribution of video content over broadband core114wireless networks is the 3GPP Multimedia Broadcast and Multicast Service (MBMS). MBMS enables distribution of video and other content from a single source (content provider)101to multiple end-users (UEs)102simultaneously with efficient utilization of radio resources.

The second controller106comprises an evolved packet core (EPC) network function virtualization (NFV) controller106and orchestrator and a second controller106is further communicatively coupled to a control plane120and a data plane module107, wherein data plane module107comprises a serving gateway (S-GW)111and a packet data node gateway (P-GW)112to route data through the mobile communication network and to manage quality of service of viewing a video in one or more of the end-user devices102.

In an embodiment, the EPC106comprises a Serving Gateway (S-GW)111, a packet data node gateway (P-GW) gateway112, and a NAT (Network Address Translator) firewall119. The eNB (Evolved Node B) (the base station)122uses the S1-AP (Application Protocol) protocol on an S1-MME interface with the MME113for control plane traffic120, and the eNodeB122uses the General Packet Radio Service (GPRS) Tunneling Protocol (GTP) on an S1-U interface with the S-GW111for user plane traffic. As used herein, S1-AP refers to services between MME113and eNB122, for example, mobility functions for UE102, Paging, Reset functionality, NAS signaling transport function, error reporting, UE102context release function, and status transfer. As used herein S1-U interface refers to an interface where S-GW111supports the S1-U interface with eNodeB122and S5/S8 data plane module107with P-GW112, wherein S5/S8 interface refers to an interface were data plane module107and control plan120support S5/S8 interface with P-GW112. Collectively the S1-MME and S1-U interfaces are known as the S1 interface, which represents the interface from eNB122to the core EPC106.

The MME113is a control-node for RAN. The MME113is responsible for UE102tracking and transmissions. The MME113is responsible for choosing the S-GW111for the UE102at the initial attach and at time of intra-LTE handover involving Core Network (CN) node relocation. The MME113is also responsible for authenticating the user by interacting with a Home Subscriber Server (HSS)115. The MME113checks the authorization of the UE102on a service provider's Public Land Mobile Network (PLMN), handles the security key management, provides the control plane120function for mobility between LTE and 2G/3G access networks with the S3 interface terminating at the MME113from a Serving GPRS Support Node (SGSN), terminates the S6a interface towards the home HSS115for roaming UE102. The HSS115is a central database that contains user-related and subscription-related information, wherein “S6a interface” refers to an interface between MME113and HSS115. The functions of the HSS115include functionalities such as mobility management113, call and session establishment support, user authentication, access authorization.

The S-GW111routes and forwards the end-user102data, while also acting as the mobility anchor for the user plane during inter-eNodeB122handovers and as an anchor for mobility between LTE and other 3rd Generation Partnership Project (3GPP) technologies. The S-GW111manages and stores UE102data, for example, of the IP service, network internal routing information. The P-GW112provides connectivity from the UE102to data networks by being the point of exit and entry of traffic for the UE102. The UE102may have simultaneous connectivity with more than one P-GW112for accessing multiple network data. The P-GW112performs policy enforcement, data filtering for each user.

The P-GW112connects to a Policy and Charging Rules Function (PCRF)117device, operator services and the network. The PCRF device117is responsible for policy control decision-making, as well as for controlling the flow-based charging functionalities in a Policy Control Enforcement Function (PCEF) via Gx interface, which resides in the P-GW112. Wherein Gx interface is used by the P-GW to communicate with the Policy and Charging Rules Function (PCRF) device117in order to handle Policy and Charging Rules. The PCRF device117provides quality of service (QoS) authorization, which decides how a data flow is treated in the PCEF and ensures that this is in accordance with the subscriber profile repository (SPR)116, wherein SPR116includes user's subscription plan, post-paid charges, pre-paid charges, etc. SPR116and PCRF device117uses Sp interface to interact with each other, herein Sp interface refers to an interface that support PCRF device117to receive data from SPR116The operator services include a network operator's IP services such as IP Multimedia Subsystem (IMS), Packet Switched Streaming (PSS), etc. The network can be the Internet121or any other network including content such as video for streaming through the wireless network to the UE102. Wherein P-GW112uses Gz interface to communicate with the offline charging system (non-real time), to send the charging records of the post-paid users and P-GW112also uses Gy interface to communicate with the online charging system (real-time) to send the charging records of the pre-paid users.

In an alternate embodiment, broadband core114is coupled to the sensing unit103and is also coupled to OTT service provider101via NAT firewall119, wherein sensing unit103receives a plurality of data from the broadband core114and bypasses the control plane120and data plane module107.

Quality of received video in networks, for example RAN networks, depends on a very large set of variables and data, for example, a video code, resolution, bandwidth, transcoding and transrating, network buffer sizes, radio interface available capacity, subscriber device capabilities, for example, screen size, resolution, subscription level, etc. The system100utilizes machine learning algorithm for prediction of video performance at UE102over mobile communication network, herein the machine learning algorithm includes traditional batch mode learning methods comprises decision trees, support vector machines, Bayesian networks, clustering, ensemble learning algorithms, and Markov Chain Monte Carlo (MCMC) algorithms, supervised, semi-supervised or unsupervised learning mode as well as versions of these algorithms adapted to data stream processing.

Based on the video quality prediction, the machine learning algorithm provides an actionable recommendation to the mobile communication network for addressing QoE issues. Herein actionable recommendation comprises, scaling the resolution or bitrate of the video stream based on the user's subscription plan, etc. These actionable recommendations can be configured to be automatically implemented by the mobile communication network or provided as suggestions for operator approval prior to implementation.

For example, the machine learning algorithm receives video QoE issues from UE102, when a user is trying to access a video from his/her mobile device. The sensing unit103receives the mobile IP and MSISDN of the UE102provided by the DHCP server of the mobile network operator (MNO). Simultaneously, sensing unit103receives URL of the video and video attributes for which user is viewing a video on his/her device102. All these data are feed into the machine learning algorithm for allocating required resolution and/or bandwidth for the video which user wants to view on her/his device102.

The Sensing unit103transmits a request to the MNO, AAA server118and PCRF device117to check the mobile plans of the UE102based on the MSISDN and mobile IP information, so as to provide the bandwidth and/or resolution that the user should get based on his/her plan or a group plan or subscribed plan in a real time.

The processing unit108is operably coupled to the sensing unit103, wherein receiving the sensed data from the sensing unit103to process the data. The Processing unit108analyzes this sensed data to determine that the resolution of the video the user has chosen (or its corresponding bandwidth equivalent) is above a threshold value, if end-user102chooses the resolution limit of the video as above a threshold value, provide adjustments while allocating resolution and/or bandwidth of the video to UE102over a mobile communication network, wherein threshold value refers to the subscription plan of the end-user102, for example, the resolution of the video the user102has chosen is less than or equal to his/her subscribed mobile plan, and notify user102that the maximum allowable resolution (and/or corresponding bandwidth) of video they can view is limited as per their subscribed plan. This involves adding a rule to the existing PCRF device117rules, as the PCRF device117provides QoS authorization (QoS class identifier and bit rates) that decides how a certain data flow will be treated in the PCEF and ensures that this is in accordance with the user's subscription plan and also could be requiring modifications in eNodeB122data. By notifying the user102about their maximum allowable resolution, conservation of the bandwidth or resolution of the video is achieved.

The system100comprises an acting unit109, wherein an acting unit109is operably coupled to the processing unit108. This acting unit109receives processed data from the processing unit108and collects a set of instructions from a machine learning algorithm based on the analysis of the processed data. In one embodiment, interface with the NAT119of the MNO, the acting unit109does the mapping of the mobile IP and MSISDN; accordingly, the acting unit109instructs the OTT platform101to stream video, applying adaptive bitrate video and transcoding if required to the video content, to the particular mobile IP and port mapping combination, and transmit the video content to the UE102with the adjustments in resolution and/or bandwidth of the video as per user subscription plan, wherein adaptive bit rate video (ABR) adjusts video stream quality in real time by detecting the user's available bandwidth throughout the video session. In ABR, the source content is compressed at multiple bit rates. Each bit rate stream is then partitioned into multiple segments and stored in the server. The stream set has designated segment duration (typically between 2 and 10 seconds) and is divided into uniform segments accordingly. Upon getting a request, the streaming user is provided with a manifest file with the available bit rate streams and the segments of the streams. The user, depending on implementation, selects the video rate that is most appropriate for its needs based on network conditions. In addition to maximizing QoE by minimizing video pauses due to buffering issues when watching a video over an unmanaged network, ABR attempts to provide a good viewing experience to user devices102.

In one embodiment, using the Interface with the NAT119of the MNO, the system100does the mapping of the mobile IP and MSISDN; accordingly, the acting unit109of the system100instructs the OTT platform101to stream to the particular mobile IP and port mapping combination, transmit the video file to the UE102with the adjustments in resolution and/or bandwidth of the video as per user subscription plan.

The adjustment in the resolution and/or bandwidth of the video as per user subscription plan provides optimizing the resource utilization at OTT platform101.

In an alternate embodiment, transcoding and/or transrating for the video file of the UE102is required at the OTT platform101, if modification in the bandwidth and/or resolution of the video is not able to apply.

The streaming video, adaptive bitrate video and transcoding of the video required on the content are transmitted from origin place (OTT platform)101to the UE102via Content Distribution Networks (CDN)110.

In another embodiment, sensing unit103receives video data from the mobile UE102, for example, cancellation of the video, stops, pauses (for more than a configurable time) or ends the video viewing or quits the video viewing application. Based on these video data, additionally added rules in the PCRF device117will be removed automatically. This removing of additional rules from PCRF device117ensures that user gets the required bandwidth of the video, only when the user102wants to access the video file. This conserves the resources at UE102by providing the required bandwidth and/or resolution of the video over a mobile communication network.

The system100is operably coupled to the machine learning algorithm, which keeps on collecting the feedback from the UE102, when a user is accessing the video file from the UE102. This machine learning performs fine-tuning of its algorithm as per user capabilities of utilizing video resources over the network.

FIG. 2illustrates a method for optimizing resource utilization201in an over-the-top (OTT) platform101to improve Quality of Experience of viewing a video in one or more of the end-user devices102in a mobile communication network. The method disclosed herein employs the system100comprising a sensing unit103configured to the processing unit108for providing quality of experience of viewing a video on the user devices102. The processing unit108is coupled to an acting unit109to determine changes required in a resolution of the video to end-user devices102and an acting unit109commands the OTT101to transmit video over a constant bandwidth and/or resolution to each of one or more of the end-user devices102, which prevents transcoding and transrating at OTT platform101and conserver resource utilization at OTT platform101.

The system100comprising a sensing unit202,103receiving a plurality of sensory input data from one or more of the end-user devices102over a network, for example, Quality the user is experiencing, Mobile Station International Subscriber Directory Number (MSISDN) and Mobile IP of the end-user devices102, information on radio link strength and quality (RSSI and CINR) from the Mobiles, traffic data, for instance, video statistics, video contents consumed by the end-user device and/or their URLs, when the user is login to view and/or consume the content, current bandwidth and/or the quality of the said videos, the times of viewing, whether any buffering occurred, total number of video bytes consumed, subscriber location, for example, Location Area Code (LAC) and Tracking Area Code (TAC), Node-B/e-Node B details including the Cell IDs, policy applied, user profile, usage of data, policy updates etc., resource utilization in the RAN including radio resources and computational resources comprises, CPU utilization. The sensing unit103also receives the actual QoE (Quality of Experience) and the desired QoE obtained in real-time which will be an index that is derived from one or multiple of the above-mentioned data, and also receives input from the user when he/she wants to get enhanced QoE.

In an embodiment, sensing unit202,103receives a plurality of sensory data from mobile network operator, for example, mobile station international subscriber directory number (MSISDN), wherein mobile network operator provides MSISDN to each one or more of the end-user devices102to identify a mobile phone number internationally, and the mobile internet protocol (IP) address. The network in the system addresses a mobile communication network and/or radio access network (RAN) as defined in 3GPP standards, with NodeBs and/or eNodeBs, the Evolved Packet Core (EPC) including the MME113, S-GW111and P-GW112. In an embodiment, a rule engine is provided. The rule engine comprises rules defined by a mobile network operator. Furthermore, the rule engine is configured to receive updated rules from the mobile network operator. In an embodiment, the rule engine comprises a machine learning algorithm. The rule engine receives the sensed data from the one or more of the end-user devices102, the mobile communication network, and the OTT platform101. The rule engine is configured to determine changes required for the video provided by the OTT platform101to the end-user devices102, if resolution of said video is below a threshold value. The rule engine is configured to set rules for handling complex network slicing operations, depending on the capability of the mobile communication network. For complex network slicing operation, the rule engine is configured to change the set of rules by changing the scheduling policies at the mobile communication network based on the needs of the users102.

In an embodiment, a sensing unit202,103is receiving a plurality of sensory input data from the content provider101. The “Content” category includes a content server which stores, retrieves and presents content, for instance, multimedia, or a CDN (Content Delivery Network) server110, an OTT (Over-The-Top) content server101or an e-commerce website. Resources at content provider comprise transcoding and transrating resources, computational resources comprises CPU utilization and memory, bandwidth information, real-time vs. non-real-time information, current latency obtained by the end-user device102for the service being given to the UE102, sensing the current security method and/or algorithm used for the service being given to the UE102is transmitting sensory data to the processing unit108for processing. The OTT101content delivery is over the network where the “Network” could be one or more of, but not limited to, a radio access network (RAN), the Mobile Network in 3GPP standards, with Node-Bs/e-Node-Bs, the Evolved Packet Core including the MME, S-GW and P-GW.

The system100comprises a processing unit203,108receives a plurality of sensory input data from the sensing unit103for processing. The processing unit108and the rule engine are on a cloud server in a cloud computing environment. As used herein, cloud computing environment refers to a processing environment comprising configurable computing physical and logical resources, for example, networks, servers, storage, applications, services, etc., and data distributed over a network. The cloud computing environment provides on-demand network access to a shared pool of the configurable computing physical and logical resources.

The sensed data, for example, QoE of the video view by the user, video statistics, video contents consumed by the end-user device102and/or their URLs, when the user login to view and/or consume the content, current bandwidth and/or the quality of the said videos, the times of viewing, whether any buffering occurred, the total number of video bytes consumed, received from one or more of the end-user devices, transcoding and transrating resources, computational resources comprises CPU utilization and memory, bandwidth information, real-time vs. non-real-time information, current latency obtained by the end-user device120for the service being given to the UE102received from OTT content provider101and MSISDN and the mobile internet protocol (IP) address received from mobile network operator are processed and executed by machine learning algorithm in the system100, for determining changes in the network to optimize resources at OTT platform101in a mobile communication network.

In an embodiment, the processing unit203,108operably coupled to the first controller104, wherein the first controller104refers to Cloud and/or centralized radio access network (CRAN) and orchestrator, CRAN connects UE102to the cellular antenna, process the data and transmit it to the mobile communication network, herein orchestrator104refers to a Software-defined networking (SDN) Orchestration is the process of automatically programming the behavior of the network, so that the network smoothly coordinates with the hardware and the software elements to further support applications and services. The first controller104transmits processed data to the off-loader module105. The processed data are then transmitted to the UE102as per user capabilities of utilizing video resources over the network. Another key service for distribution of video content over broadband core114wireless networks is the 3GPP Multimedia Broadcast and Multicast Service (MBMS).

In another embodiment, the second controller106is operably coupled to the processing unit108, wherein the second controller106comprises the evolved packet core (EPC) network function virtualization (NFV) controller and orchestrator and the second controller106is further communicatively coupled to a data plane module107, wherein data plane module107comprises serving gateway (S-GW)111and a packet data node gateway (P-GW)112to route data through the mobile communication network and to manage quality of service of viewing a video in one or more of the end-user devices102.

In another embodiment, the EPC106includes a Serving Gateway (S-GW)111, a Packet Data Network (P-GW) gateway112, and a NAT firewall119. The eNB122uses the S1-AP (Application Protocol) protocol on an S1-MME interface with the MME113for control plane traffic, and the eNB122uses the General Packet Radio Service (GPRS) Tunneling Protocol (GTP) on an S1-U interface with the S-GW111for user plane traffic. Collectively the S1-MME and S1-U interfaces are known as the S1 interface, which represents the interface from eNB122to the core EPC106.

The MME113chooses the S-GW111for the UE102at the initial attach and at time of intra-LTE handover involving Core Network (CN) node relocation. The MME113checks the authorization of the UE102on a service provider's Public Land Mobile Network (PLMN), handles the security key management, provides the control plane120function for mobility between LTE and 2G/3G access networks with the S3 interface terminating at the MME113from a Serving GPRS Support Node (SGSN), terminates the S6a interface towards the home HSS115for roaming UEs102, and the like. The HSS115is a central database that contains user-related and subscription-related information. The functions of the HSS115include functionalities such as mobility management, call and session establishment support, user authentication, access authorization.

The S-GW111routes and forwards the user data in the mobile communication network. The P-GW112provides connectivity from the UE102to data networks.

The P-GW112connects to a Policy and Charging Rules Function (PCRF)117device, operator services, and the network. The PCRF device117is responsible for policy control decision-making, as well as for controlling the flow-based charging functionalities in a Policy Control Enforcement Function (PCEF), which resides in the P-GW112. The PCRF device117provides QoS authorization (QoS class identifier and bit rates) that decides how a certain data flow will be treated in the PCEF and ensures that this is in accordance with the user's subscription plan. The operator services can include a network operator's IP services such as IP Multimedia Subsystem (IMS), Packet Switched Streaming (PSS), etc. The network can be the Internet121or any other network including content such as video for streaming through the wireless network to the UE102.

In another embodiment, the processing unit203,108transmits the processed data to OTT platform101, wherein OTT platform101is operably coupled to the sensing unit103, wherein receiving the sensed data from the sensing unit103to process the data. The processing unit108process the data to determine that the resolution of the video the user102has chosen (and/or corresponding bandwidth equivalent) is less than or equal to his/her subscribed mobile plan, and notify user that the maximum allowable resolution (and/or corresponding bandwidth) of video he/she can view is limited as per their subscribed plan. This involves adding a rule to the existing PCRF rules117, as the PCRF device117provides QoS authorization that decides how a certain data flow will be treated in the PCEF and ensures that this is in accordance with the user's subscription profile and also could be requiring modifications in eNode-B122data. By notifying the user102about their maximum allowable resolution, conserving of the bandwidth or resolution of the video is achieved.

The system204,100comprises an acting unit109, wherein an acting unit109is operably coupled to the processing unit108. This acting unit109receives processed data from the processing unit108and collects a set of instructions from the machine learning algorithm based on the analysis of the processed data. In another embodiment, interface with the NAT119of the MNO, the system100does the mapping of the mobile IP and MSISDN; accordingly, the system100transmit instructs to the OTT platform101to stream video, applying adaptive bitrate video and transcoding if required to the video content, to the particular mobile IP and port mapping combination, and transmit the video file to the UE102with the adjustments in resolution and/or bandwidth of the video as per user subscription plan, wherein adaptive bit rate video (ABR) adjusts video stream quality in real time by detecting the user's available bandwidth throughout the video session. In ABR, the source content is compressed at multiple bit rates. Each bit rate stream is then partitioned into multiple segments and stored in the server. The stream set has designated segment duration (typically between 2 and 10 seconds) and is divided into uniform segments accordingly. Upon a GET request, the streaming client is provided with a manifest file with the available bit rate streams and the segments of the streams. The client, depending on implementation, selects the video rate that is most appropriate for its needs based on network conditions. In addition to maximizing QoE by minimizing video pauses due to buffering issues when watching a video over an unmanaged network, ABR attempts to provide a good viewing experience to user devices102.

The adjustment in the resolution and/or bandwidth of the video as per user subscription plan helps in optimizing resource utilization (of transcoding/transrating resources) at the OTT platform101.

In an alternate embodiment, transcoding and/or transrating for the video file of the UE102is required at the OTT platform101, if modification in the bandwidth and/or resolution of the video is not applied.

In another embodiment, sensing unit103receives video data from the UE102, for example, cancellation of the video, stops, pauses (for more than a configurable time) or ends the video viewing or quits the video viewing application. Based on these video data, additionally added rules in the PCRF117will be removed automatically. This erases of additional rules from PCRF117, to ensure that the user gets the bandwidth of the video, only when the user wants to access the video file. This conserver the resources at one or more of the end-user devices102by providing the required bandwidth and/or resolution of the video over a mobile communication network.

In another embodiment, machine learning algorithm keeps on collecting the feedback from the UE102, when user is accessing the video file. This machine learning performs tweaking of its algorithm as per the user needs.

FIGS. 3A and 3Billustrate a flowchart comprising the steps performed by system100to optimize resource utilization in an over-the-top (OTT) platform101over a mobile communication network. The system100is operably coupled to the sensing unit301to receive a plurality of inputs from the end-user devices102, for example, mobile station international subscriber directory number (MSISDN) of one of an individual end-user devices102and mobile internet protocol (IP) address, wherein mobile network operator provides MSISDN to each of one or more of the end-user devices102to identify a mobile phone number internationally. The Sensing unit301,103also receives URL of the video and video attributes for which user is viewing on his/her device102, the sensed data received by the sensing unit301,103is provided to the machine learning for processing the data.

The Sensing unit302,103transmits a request to the MNO, AAA server118and PCRF device117to check the Mobile Plans of the UE102based on the MSISDN and mobile IP information, so as to provide the bandwidth and/or resolution that user should get based on his/her plan or a group plan or subscribed plan in a real time.

The processing unit303,108is operably coupled to the sensing unit301,302,103, wherein receiving the sensed data from the sensing unit103to process the data. The processing unit108is communicatively coupled to the machine learning algorithms via rule engine. This rule engine is coupled to the mobile network operator wherein, mobile network operator provides user device102details to the rule engine. This rule engine includes a set of rules that are based on the application running on the user devices102, for example, video application, resolution of the video, bandwidth for the video, etc.

The machine learning algorithms stored in the rule engine checks304the resolution of the video the user device102has chosen (or its corresponding bandwidth equivalent) is above a threshold value. If end-user chooses the resolution limit of the video as above a threshold value, then machine learning algorithm provides adjustments while allocating resolution and/or bandwidth of the video to UE102over a mobile communication network, wherein threshold value refers to the subscription plan of the end-user102, wherein subscription plan includes the maximum resolution for the video that can be viewed on the user devices102.

The machine learning algorithm provides a set of rules to reserve305the bandwidth resources to the Policy and charging rules function device117based on the user demand for the resolution of the video.

Reserving of the bandwidth involves adding306a rule to the existing PCRF device117rules, as the PCRF device117provides QoS authorization that decides how a data flow will be treated and ensures that this is in accordance with the user's subscription profile and could also be requiring modifications in eNodeB122data, this notifies the user to view the video with maximum allowable resolution as per their subscription plan for conserving the bandwidth and/or resolution of the video over a mobile communication network

The machine learning performs307mapping of received sensed data, for example, mobile internet protocol IP and mobile station international subscriber directory number MSISDN and simultaneously ensures the resolution of the video chosen by end-user devices102.

The machine learning instructs308OTT platform101to stream video to the particular mobile IP and port mapping combination, by transmitting the video file to the UE102with the adjustments in resolution and/or bandwidth of the video according to the user subscription plan. The adjustment in the resolution and/or bandwidth of the video is performed as per rules added to the exiting PCRF device117for reserving the bandwidth as in step305. If the rules which are added to the exiting PCRF device117for reserving the bandwidth is not achieved by the required adjustments in the resolution and/or bandwidth of the video, then transcoding and/or transrating is performed on the video file. Hence, optimization of resource utilization is achieved by preventing transcoding and transrating performance on the video file of the user devices102.

As per optimizing the resource utilization at OTT platform101, machine learning analyzes309the video data from the user devices102, for example, cancellation of the video, stop time, pause for more than a configurable time for video viewing application. Based on these video data, additionally added rules in the PCRF device117will are removed automatically.

This removing of additional rules from PCRF device117ensures that user device102gets the required bandwidth of the video. Allocate310the required bandwidth for the video file, only when one or more of the end-user devices102wants to access the video file. This conserves the resources at the user devices102by providing the required bandwidth and/or resolution of the video over a mobile communication network only when required.