Patent Publication Number: US-10791026-B2

Title: Systems and methods for adaptive over-the-top content quality of experience optimization

Description:
FIELD OF THE DISCLOSURE 
     The present disclosure generally relates to networking systems and methods. More particularly, the present disclosure relates to systems and methods for adaptive Over-the-Top (OTT) content Quality of Experience (QOE) optimization. 
     BACKGROUND OF THE DISCLOSURE 
     OTT content includes delivery of audio, video, and/or other media over the Internet without the involvement of a Service Provider in the control or distribution of the content. The Service Provider (e.g., network operators, Multiple-System Operator (MSO), etc.) may be aware of the content of OTT-related packets, but generally is not responsible nor able to control, the viewing ability, copyright, or other redistribution of the content. OTT content is proliferating and expected to be the distribution model of the future. Conventionally, OTT content has followed a process of creating content and then publishing the content in a variety of encodings. The published content is then delivered through a plurality of networks such as the Internet, Private Networks, Content Providers (CP), Cloud Networking Providers, Content Distribution Networks (CDN), Internet Service Provider (ISP) networks, and the like. These networks have optical, packet, and higher layer components and capabilities to facilitate the delivery of the content to end clients. The popularity of content and user satisfaction is often tracked based on delivery server, social media, and other data. Programming departments may use this data to determine what content is most valued by an audience. At the same time, many of the networks involved in the delivery are often evaluated based on throughput, latency, availability, and susceptibility to attack. 
     For feedback, Quality of Experience (QOE) mechanisms have emerged that deliver user session specific detail by integrating functionality into the client video player which may be coupled with key information from the content provider. QOE is a measure of a customer&#39;s experiences with a service. QOE focuses on the entire service experience and is a more holistic evaluation than the more narrowly focused user experience (focused on a software interface) and customer-support experience (support focused). 
     QOE is just starting to be measured, but little is done with QOE except that the content provider may manually select an alternate CDN based on the QOE or adjust the configuration of the OTT Video Platform (OVP). Conventionally, OTT video QOE does not impact the optical or packet network configuration or the compute (such as Network Functions Virtualization (NFV)) infrastructure that may be the cause of the low QOE. This lack of QOE visibility and action at the optical, packet, or NFV components leads to higher cost and lower performance in OTT content delivery. When the source of the poor quality, for example, the physical network, cannot be adapted by the content provider, they will often choose to work around the problem (e.g., alternate CDN, buy dark fiber, etc.) which is ultimately more expensive. Of course, failure to act on the QOE problems in the network (optical, packet, compute, NFV, etc.) results in a lower performance solution. 
     Content providers and service providers with access to the QOE information may currently employ analysts to identify problems with published or cached information or with CDNs. The information is typically used to address customer complaints but may also result in manually changing the OVP configuration that controls the content publishing and CDN selection. 
     BRIEF SUMMARY OF THE DISCLOSURE 
     In an exemplary embodiment, a method for adaptive Over-the-Top (OTT) content optimization based on Quality of Experience (QOE) is implemented in an OTT optimization platform communicatively coupled to a plurality of devices in a network. The method includes receiving a plurality of inputs including network inputs, service and software inputs, and QOE inputs; analyzing the plurality of inputs with respect to one or more OTT content streams to perform an optimization thereof; determining adjustments in the network based on the optimization; and one of notifying a network operator of the adjustments and automatically causing the adjustments in the network. The network can include a plurality of an optical layer, a packet layer, a compute layer, and a video functions layer, and wherein the adjustments can include changes to one or more of i) physical and virtual network elements in the optical layer, ii) physical and virtual network elements in the packet layer, iii) servers and other compute elements in the compute layer, and iv) physical and virtual resources in the video functions layer. The network inputs and the service and software inputs can be received from a management plane, and wherein the QOE data is received from a database which manages the QOE data from players associated with user devices. 
     The network can include a combination of OTT delivery elements to end users and OTT content publishing elements to provide the OTT content streams to the network, and wherein the optimization can include evaluating tradeoffs for configured of the OTT delivery elements and the OTT content publishing elements based of a plurality of factors including revenue generation, cost, resource use, function, risk, location, and impact on user QOE. The receiving can be via one or more Application Programming Interfaces (APIs) from a management plane associated with the network or directly from the network. The QOE inputs can be related to one of an individual OTT content stream to a user, a selected representative OTT content stream to a selected user amongst a group of users associated with a corresponding group of OTT content streams, and an aggregate QOE for the group of OTT content streams, and wherein the adjustments correspond to one of the individual OTT content stream and the group of OTT content streams. 
     The adjustments can include a determination of caching in the network based on the OTT content to improve the QOE inputs. The determining can include a determination of a particular file for OTT content is improperly encoded on a cache, and wherein the adjustments can include deletion of the file and an increase of bandwidth back to an origin server until caching of the file stabilizes. The optimization can include an evaluation of various tradeoffs to determine the adjustments, and wherein the evaluation is based on the receiving, historical inputs, forecasted use, and user defined inputs. The tradeoffs can be based on QOE versus one or more of compute resources, bandwidth, latency, and encoding of the OTT content. The QOE data can be consolidated by a system receiving feedback from a plurality of players associated with user devices consuming the OTT content. 
     In another exemplary embodiment, an optimization platform adapted for Over-the-Top (OTT) content optimization based on Quality of Experience (QOE) includes a network interface communicatively coupled to a network; a processor communicatively coupled to the network interface; and memory storing instructions that, when executed, cause the processor to receive a plurality of inputs including network inputs, service and software inputs, and QOE inputs, analyze the plurality of inputs with respect to one or more OTT content streams to perform an optimization thereof, determine adjustments in the network based on the optimization, and one of notify a network operator of the adjustments and automatically cause the adjustments in the network. The network can include a plurality of an optical layer, a packet layer, a compute layer, and a video functions layer, and wherein the adjustments can include changes to one or more of i) physical and virtual network elements in the optical layer, ii) physical and virtual network elements network elements in the packet layer, iii) compute resources in the compute, and iv) physical video relevant appliances or video virtual functions in the video functions layer. 
     The network inputs and the service and software inputs can be received from a management plane, and wherein the QOE data is received from a database which manages the QOE data from players associated with user devices. The network can include a combination of OTT delivery elements to end users and OTT content publishing elements to provide the OTT content streams to the network, and wherein the optimization can include evaluating tradeoffs for configured of the OTT delivery elements and the OTT content publishing elements based of a plurality of factors including revenue generation, cost, resource use, function, risk, location, and impact on user QOE. The receiving can be via one or more Application Programming Interfaces (APIs) from a management plane associated with the network. The QOE inputs can be related to one of an individual OTT content stream to a user, a selected representative OTT content stream to a selected user amongst a group of users associated with a corresponding group of OTT content streams, and an aggregate QOE for the group of OTT content streams, and wherein the adjustments correspond to one of the individual OTT content stream and the group of OTT content streams. The optimization can include an evaluation of various tradeoffs to determine the adjustments, and wherein the evaluation is based on the receiving, historical inputs, forecasted use, and user defined inputs. The QOE data can be consolidated by a system receiving feedback from a plurality of players associated with user devices consuming the OTT content. 
     In a further exemplary embodiment, a non-transitory computer readable medium includes instructions executable by a processor, and in response to such execution causes the processor to perform operations including receiving a plurality of inputs including network inputs, service and software inputs, and QOE inputs; analyzing the plurality of inputs with respect to one or more OTT content streams to perform an optimization thereof; determining adjustments in the network based on the optimization; and one of notifying a network operator of the adjustments and automatically causing the adjustments in the network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated and described herein with reference to the various drawings, in which like reference numbers are used to denote like system components/method steps, as appropriate, and in which: 
         FIG. 1  is a network diagram and flow diagram of a network and OTT content process describing OTT content from creation to delivery; 
         FIG. 2  is a network diagram of a network illustrating content distribution to an end user; 
         FIG. 3  is a flowchart of an OTT content optimization method implementable via the optimization platform; 
         FIG. 4  is a diagram of a feedback loop formed between the QOE data, the optimization platform, and the network components for OTT content publishing and delivery; and 
         FIG. 5  is a block diagram of an exemplary implementation of a server, which can be used to implement the optimization platform and/or the OTT content optimization method. 
     
    
    
     DETAILED DESCRIPTION OF THE DISCLOSURE 
     Again, in various exemplary embodiments, the present disclosure relates to systems and methods for adaptive Over-the-Top (OTT) Quality of Experience (QOE) optimization. The systems and methods cover the automated allocation and continually re-adjusted allocation of the resources (e.g., optical, packet, NFV, etc.) used for over the top (OTT) content publishing and delivery based on the user Quality of Experience and the network economics. This is a form of analytics. The systems and methods include an OTT optimization platform and OTT optimization process that closes a feedback loop between the QOE data and the underlying network. Various adjustments are contemplated herein to optimize OTT content delivery based on real-world QOE feedback. 
     OTT Content Creation to Delivery in a Network 
     Referring to  FIG. 1 , in an exemplary embodiment, a network diagram and flow diagram illustrate a network  10  and OTT content process  12  describing OTT content from creation to delivery. The OTT content process  12  includes four major steps for OTT content to end in the hands of the end user. Specifically, the OTT content process  12  includes content creation  14 , content publishing  16 , content delivery  18 , and content viewing  20 . The content creation  14  is where the OTT content, e.g., video, audio, etc., is recorded, generated, etc. The content publishing  16  is the process of preparing the OTT content for consumption and includes such aspects as encoding, static advertisement insertion, pricing, Digital Rights Management (DRM), user and device management, and the like. The content delivery  18  is the process of transmitting the OTT content from its origin or hosting location to the destination device. This can be performed live or on demand and can be streamed, where delivery is in parallel with viewing, or downloaded, where the OTT content is completely delivered prior to viewing. The content viewing  20  is the process of displaying or consuming the OTT content on a physical device. 
     The content delivery  18  is over the network  10  which includes one or more networks which can have different layers, such as an optical layer  22 , a packet layer  24 , a compute layer  26 , and a video functions layer  28 . The optical layer  22  can include optical network elements operating at Layer 0 (photonic—Dense Wave Division Multiplexing (DWDM)) and/or Layer 1 (Time Division Multiplexing (TDM) such as Optical Transport Network (OTN)). The packet layer  24  can include packet network elements such as switches, routers, etc. operating at Layer 2 and/or 3, such as Ethernet, Multiprotocol Label Switching (MPLS), Internet Protocol (IP), and the like. The compute layer  26  can include dedicated compute capable hardware and/or Virtual Network Functions (VNF) via compute elements which executed the VNFs in data centers, via fog computing, etc. The video functions layer  28  can perform content oriented software functions, such as Content Distribution Networks (CDN), virtual CDNs (vCDN), caching, etc. 
     The network elements and compute elements in the layers  22 ,  24 ,  26 ,  28  can be managed by a management plane  30  and an OTT Video Platform (OVP) and OTT analytics (OVA) platform  32 . The management plane  30  can be one or more of a network orchestrator, Software Defined Networking (SDN) Controller, a Control Plane, or the like. If the management plane  30  include a network orchestrator, a Network Management System (NMS), and/or OVP, these devices can interface to a Billing, Customer Records Management (CRM), and Customer Provisioning system  34 . 
     As the OTT delivery  18  crosses multiple networks, via the layers  22 ,  24 ,  26 ,  28 , network quality of service (QOS) measurement services  36  have been deployed, primarily in the cloud, to evaluate how the network  10 , especially cloud services, commercial Content Delivery Network (CDN), and Internet Service Provider (ISP) networks, are performing. 
     However, these network measurements are purely in aggregate and do not enable the provider or any of the delivery network  10  to understand the user quality of experience. For example, if a user called to complain about a particular OTT video&#39;s delivery, until recently, there was no mechanism for the content provider or any of the delivery networks to understand what experience that exact OTT video session had experienced. Recently, some video players have had the quality of experience measurement transmission integrated into a cloud computing system to create a database of the user video session experiences and to analytically analyze that user QOE data  38 . 
     In various exemplary embodiments, a quality/economics optimization platform  40  completes the feedback cycle by taking the aggregate and individual QOE data  38  measurements and analyzes them to understand how to obtain a target level of OTT video publishing and/or delivery quality and revenue with the lowest cost. The platform  40  continuously adapts to OTT video publishing and/or network state to adjust the cost versus quality/revenue equation. 
     The platform  40  can interface to various components in the network  10  via Application Programming Interfaces (APIs). Specifically, the platform  40  can receive service and aggregate user QOE information from the QOE data  38  and the platform  40  can receive network service quality from the QOS measurements  36 . The platform  40  can provide video publishing and CDN decisions to the platform  32 . Further, the platform  40  can interface to the management plane  30  for video delivery network configuration decisions and to receive network state updates. These APIs allow the platform  40  to identify the resources allocated to OTT content delivery, the resources which could be allocated to OTT content delivery, the state of the OTT content delivery networking and publishing solution, and the quality of service and user quality of experience that is measured with the current network state in the network  10 . Further, where the network orchestration, control, and management plane  30  lacks key adaptive capabilities, it may be necessary for the platform  40  to interface directly with any device or function in the network  10 . 
     The platform  40  operates on the principle that the optimal state for the OTT content is one where QOE, revenue, and cost (e.g., capital expense (CAPEX), operating expense (OPEX), and/or administrative values) are optimized in accordance with policy. The platform  40  leverages policy and preconfigured cost and revenue information to process the target User QOE, compare against policy, evaluate alternative resource and software configurations in the network  10 , and adjust the state of the OTT video delivery network  10  and the OTT video publishing functions of the OVP to achieve the target QOE with the minimal cost/maximum revenue. 
     In essence, the platform  40  is a series of APIs and an analytics engine operating on a processing apparatus. The analytics engine is driven by a variety of processes to trade off some or all components of OTT content delivery elements and OTT content publishing elements based on an evaluation of their revenue generation, cost, resource use, function, risk, location, impact on user QOE, and other attributes. 
     By analyzing the User QOE and/or the network QOS data in the context of the network  10  and OVP state, the analytics engine in the platform  40  can decide whether to increase, reduce, or maintain the resources used for OTT content delivery in the network  10 . An increased investment would be targeted at increasing resources in the manner that is most effective to increase QOE and/or Revenue with the lowest cost. Similarly, reducing the resources would evaluate the network  10  in the context of minimizing the QOE and revenue impact but delivering the greatest savings. Typically, components of the solution that are overwhelmed or dysfunctional would be obvious areas for the platform  40  to invest in to improve the QOE with minimal cost impact. Underutilized areas of the network  10  or OVP solution would be obvious areas to reduce the overall solution cost. 
     Thus, by introducing the platform  40  with the analytics engine with QOE and economic policy based decision making centered around user QOE, network QOS, OVP state, and network state can deliver a more cost optimized OTT content publishing and delivery solution. When network configuration actions can be communicated to the network  10  or OVP elements quickly through a near real-time management solution, SDN, NFV, and/or orchestration, the platform  40  will enable near real-time adjustments to fix publishing and delivery solutions and continuously adjust the user QOE to be in line what the QOE and economics policy. This ensures a stronger QOE, high performance, and cost efficiency. 
     Quality of Experience (QOE) 
     Again, QOE is a measure of a customer&#39;s experiences with a service, i.e., the OTT content. QOE focuses on the entire service experience and is a more holistic evaluation than the more narrowly focused user experience (focused on a software interface) and customer-support experience (support focused). Recently, new QOE solutions have emerged that deliver user session specific detail by integrating functionality into the client video player (client) which may be coupled with key information from the content provider. This QOE detail may be provided through events, measurements, and the environment detected through the client or content provider. The QOE detail is often analyzed and provided to the content provider and/or delivery network providers. Forums like the Streaming Video Alliance (SVA) and the Consumer Technology Association (CTA) are forming best practices and standardizing on these metrics, events and environmental variables provided by QOE solutions. Examples of such metrics and events include the following: 
                                        delivered bitrate   labeled bitrate   player resolution       buffer length   re-buffering ratio   interruptions ratio       (current &amp; max)       player errors   network type   network signal strength       player state   exit before video count   exit before video               start ratio       video start time   video start failure   master manifest URL       variant manifest URL   play failure count   play failure ratio       In-stream failure count   play head time   bytes downloaded       throughput   sleep   Shutdown       geolocation   streaming server   client IP address           IP Address       Closed caption/   CPU utilization   GPU utilization       Subtitle quality       memory utilization   VPN presence   CDN node type       CDN edge server   Server Redirection   VPN presence       IP address       OS version   Browser info                    
Example Network
 
     Referring to  FIG. 2 , in an exemplary embodiment, a network diagram illustrates a network  50  illustrating content distribution to an end user  52 . The network  50  provides additional details of the components in the network  10 . Specifically, the network  50  illustrates one exemplary configuration to deliver an OTT content stream to the end user  52 . Components in the network  50  can include a data center/cloud site  54 , a metro node  56 , a captive office/head end  58 , and the end user  52 . The data flow for the OTT content is from the site  54  through the metro node  56  and the head end  58  to the end user  52 . The site  54  can include an origin server  60  hosting the OTT content, one or more routers/switches  62 , and a network element  64 . The origin server  60  provides the OTT content through the one or more routers/switches  62  to the network element  64 . The one or more routers/switches  62  can provide higher layer connectivity (L2 and above) and the network element  64  can provide L0, L1, L2, and/or L3 connectivity to a packet-optical network  66 . 
     The packet-optical network  66  provides long haul, regional connectivity between the site  54  and the metro node  56 . The metro node  56  provides metro connectivity and includes the network element  64  interfacing to the packet-optical network  66 . The metro node  56  can include an edge cache  68  for locally caching OTT content from the origin server  60 . Specifically, the edge cache  68  can deliver the OTT content to avoid connectivity or the latency of the packet-optical network  66 . This edge cache  68  and even the micro cache  78  may be a set of devices and functions including reverse proxy, firewall, content caching, load balancing, dynamic ad insertion, personalization, packet switching, routing, etc. The metro node  56  includes a network element  70  interfacing a metro network  72 . The metro network  72  is similar to the packet-optical network  66  albeit optimized to the metro scale. The metro node  56  includes the one or more routers/switches  62  interconnecting the edge cache  68  and the network elements  64 ,  70 . 
     The head end  58  includes the network element  70  interfacing the metro network  72 . In an exemplary embodiment, the head end  58  includes an Optical Line Terminal (OLT)  74  which can be part of a Passive Optical Network (PON) interfacing an Optical Network Terminal (ONT)  76  at or near the end user  52 . The head end  58  can further include a micro cache  78  which can locally cache the OTT content at the head end  58 . The one or more routers/switches  62  at the head end  58  can connect the network element  70  to the OLT  74 . The caches  68 ,  78  are used to reduce upstream network bandwidth and reduce latency for OTT content by storing some content, such as based on demand, locally to reduce requests back to the origin server  60 . At the end user  52 , a Set Top Box  80  or equivalent is communicatively coupled to the ONT  76  for receiving the OTT content. 
     Those of ordinary skill in the art will recognize the network  50  is presented for illustration purposes, and the network  50  can include other network technologies, configurations, etc. For example, the end user  52  in the network  50  is shown using PON and the STB  80 , but other access technologies are also contemplated, such as cable modems, wireless, etc. Also, the STB  80  could be replaced with a user device which accesses the OTT content, such as over a local wireless network. Of course, various embodiments are contemplated for the networks  10 ,  50 . 
     Optimization Method and Platform 
     Referring to  FIG. 3 , in an exemplary embodiment, a flowchart illustrates an OTT content optimization method  100  implementable via the optimization platform  40 . Again, the optimization platform  40  has connectivity to the network  10 ,  50 , such as via various APIs, and an analytics engine to perform optimization, to tweak/adjust the network  10 ,  50  based on various inputs including QOE data. The optimization method  100  includes receiving network inputs, server and software inputs, and QOE inputs (step  102 ). The network inputs are from any of the optical layer  22 , the packet layer  24 , and/or the compute layer  26 . That is, the network inputs are from any physical or virtual network elements, from the management plane  30 , or the like. Example network inputs include the following for both physical and virtual network elements: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 Topology 
               
               
                   
                 link state 
               
               
                   
                 port and logical port utilization 
               
               
                   
                 link and nodal latency 
               
               
                   
                 Jitter 
               
               
                   
                 by port and logical port transmitted, received, 
               
               
                   
                 errored and discarded frames and bytes 
               
               
                   
                 memory utilization 
               
               
                   
                 Central Processing Unit (CPU) utilization 
               
               
                   
                 network or resource congestion 
               
               
                   
                 power utilization 
               
               
                   
                 system and card temperatures 
               
               
                   
                 device attributes for physical and virtual nodes 
               
               
                   
                   
               
            
           
         
       
     
     The server and software inputs can be from the video functions layer  28  and/or the NFVI layer  26 . Example server and software inputs include the following: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 Virtual Machine (VM)/container state 
               
               
                   
                 disk capacity, quota, and utilization 
               
               
                   
                 CPU/Graphics Processing Unit (GPU) utilization and quota 
               
               
                   
                 system temperature 
               
               
                   
                 disk integrity 
               
               
                   
                 disk type 
               
               
                   
                 Operating System (OS) 
               
               
                   
                 power utilization 
               
               
                   
                 application state and attributes 
               
               
                   
                   
               
            
           
         
       
     
     The QOE inputs can be from the QOE data  38 . Example QOE inputs include the following: 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                 delivered bitrate 
                 labeled bitrate 
               
               
                 buffer length (current &amp; max) 
                 re-buffering ratio 
               
               
                 interruptions ratio 
                 player errors 
               
               
                 client network type 
                 client network signal strength 
               
               
                 player state 
                 exit before video count 
               
               
                 exit before video start ratio 
                 video start time 
               
               
                 video start failure 
                 master manifest Uniform Resource 
               
               
                   
                 Locator (URL) 
               
               
                 variant manifest URL 
                 play failure count 
               
               
                 play failure ratio 
                 In-stream failure count 
               
               
                 play head time 
                 bytes downloaded 
               
               
                 throughput 
                 client active/idle/sleep/shutdown state 
               
               
                 client geolocation 
                 streaming server Internet Protocol 
               
               
                   
                 (IP) Address 
               
               
                 client IP address 
                 Closed caption/Subtitle quality 
               
               
                 Client CPU utilization 
                 Client GPU utilization 
               
               
                 memory utilization 
                 Virtual Private Network (VPN) presence 
               
               
                 CDN node type 
                 CDN edge server IP address 
               
               
                 Server Redirection 
                 Operating System (OS) 
               
               
                 OS version 
                 Browser info 
               
               
                   
               
            
           
         
       
     
     With the inputs, the OTT content optimization method  100  analyzes the various inputs using an optimization (step  104 ), determines adjustments in the network  10 ,  50  based on the optimization (step  106 ), and causes the adjustments in the network  10 ,  50  (step  108 ). The OTT content optimization method  100  can be implemented continuously, on demand, or periodically. The intent of the optimization is to review and analyze all of the inputs including the QOE inputs and determine adjustments in the network  10 ,  50  accordingly. The adjustments can be based on reducing cost, improving quality (QOE), and the like based on various tradeoffs. 
     The OTT content optimization method  100  can continuously and/or periodically receive the inputs and maintain historical values of the inputs. Also, the OTT content optimization method  100  can have forecasted use of OTT content. Further, the OTT content optimization method  100  can have user defined inputs such as costs, location, etc. Based on the current inputs, the historical inputs, the forecasted use, and the user defined inputs, the OTT content optimization method  100  performs a qualitative evaluation of various tradeoffs to determine network adjustments. 
     In an exemplary embodiment, the optimization is performed for the following qualitative evaluations: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 QOE/Bandwidth trade-offs by qualitative evaluation, 
               
               
                   
                 forecasted use, cost, and/or location 
               
               
                   
                 QOE/Revenue trade-offs by qualitative evaluation, 
               
               
                   
                 forecasted use, cost, and/or location 
               
               
                   
                 QOE/CPU/GPU trade-offs by qualitative evaluation, 
               
               
                   
                 forecasted use, cost, and/or location 
               
               
                   
                 QOE/Memory trade-offs by qualitative evaluation, 
               
               
                   
                 forecasted use, cost, and/or location 
               
               
                   
                 QOE/Cost trade-offs by qualitative evaluation, forecasted 
               
               
                   
                 use, cost, and/or location 
               
               
                   
                 QOE/Latency trade-offs by qualitative evaluation, 
               
               
                   
                 forecasted use, cost, and/or location 
               
               
                   
                 Device reliability/Revenue trade-offs by forecasted 
               
               
                   
                 evaluation, use, cost, and/or location 
               
               
                   
                 Bandwidth/Storage trade-offs by forecasted use, 
               
               
                   
                 cost, and/or location 
               
               
                   
                 CPU/GPU/Storage trade-offs by forecasted use, 
               
               
                   
                 cost, and/or location 
               
               
                   
                 CPU/GPU/Bandwidth trade-offs by forecasted use, 
               
               
                   
                 cost, and/or location 
               
               
                   
                 Re-encoding of corrupted encoded files by cost, forecasted 
               
               
                   
                 popularity, and/or location 
               
               
                   
                   
               
            
           
         
       
     
     The adjustments in the network  10 ,  50  can be adjusting existing services in the network  10 ,  50  as well as provisioning new services. The OTT content optimization method  100  contemplates the adjustment in one or more of the layers  22 ,  24 ,  26 ,  28 . The following lists exemplary actions which the platform  40  can implement to cause adjustments in the network  10 ,  50  based on the optimization: 
     
       
         
           
               
             
               
                   
               
             
            
               
                 Logical or physical, packet or optical, Link/Port bandwidth adjustment 
               
               
                 provisioning of new packet and/or optical network connections 
               
               
                 optical or packet traffic rerouting 
               
               
                 virtual machine/container memory quota adjustment 
               
               
                 virtual machine/container memory quota 
               
               
                 new virtual machine/container deployment 
               
               
                 server redirection/substitution 
               
               
                 content caching/placement request 
               
               
                 content re-encoding 
               
               
                 content access modification 
               
               
                 operator alerts 
               
               
                 altered personalization and advertisement attributes 
               
               
                 payment adjustments 
               
               
                 end user notification 
               
               
                 CDN notification 
               
               
                 CDN switching 
               
               
                 device isolation 
               
               
                   
               
            
           
         
       
     
     The optimization platform  40  and the OTT content optimization method  100  can adjust any of the components in the network  10 ,  50  from the content publishing  16 , the content delivery  18 , and the content viewing  20 . That is, from the origin server  60  to the end user  52 , including all of the data center, core, metro, edge, and end user premises equipment in between. 
     Referring to  FIG. 4 , in an exemplary embodiment, a diagram illustrates a feedback loop formed between the QOE data  38 , the optimization platform  40 , and the network  10 ,  50  components for OTT content publishing and delivery. Again, while OTT video has been in existence for a while, the ability to measure the end user QOE by video stream is new. To date, there has been nothing to complete the feedback loop to the network  10 ,  50 . Again, the optimization platform  40  analyses the current user QOE against a set of policies that define the desired quality of experience. Note, the optimization platform  40  and the OTT content optimization method  100  can be implemented on individual user streams and their QOE, groups of user streams and either their aggregate QOE or a representative stream&#39;s QOE, or the like. 
     Once the analysis is complete, if the user QOE (an individual stream QOE, a representative stream QOE, or an aggregate QOE for a group of streams) is less than a target, the optimization platform  40  and the OTT content optimization method  100  will evaluate potential network causes for the discrepancy and pursues alternative network configurations, based on policies defined by the network operator. If better QOE would result from an alternate network configuration and/or if an action is necessary because of the analysis (e.g., SLA penalty), the optimization platform  40  and the OTT content optimization method  100  recommend and/or causes this change or action from the network operator. 
     This feedback loop can be fully automated by using APIs to suggest network changes to the network orchestration, control, and management components of the network  10 ,  50  instead of relying on network operator administration to make changes. This would convert the recommendations above to automated and adaptive network actions. 
     The feedback loop can further be extended to include OVP functions by including the OVP components and their software, storage, and hardware resource components in the analysis of the QoE and the potential causes of poor quality of experience. The optimization platform  40  and the OTT content optimization method  100  could then recommend configuration changes and/or actions from the operator, the OVP, and/or the network orchestrator, control, and management components. One of many potential scenarios is to identify a popular improperly encoded file on a cache, have that file deleted and bandwidth to the origin server  60  temporarily increased until encapsulation is regenerated and the caching of the popular content stabilizes. 
     Further, the optimization platform  40  and the OTT content optimization method  100  can evaluate cost information in the analysis. This is performed by adding a set of policies that allow the operator to define cost and revenue. The cost and revenue policies are leveraged to optimize the current and potential network and/or the OVP states that could potentially lead to the target QOE with an aim to maximize the revenue, minimize the cost and achieve the target QOE. 
     In an exemplary embodiment, a process for adaptive Over-the-Top (OTT) content optimization based on Quality of Experience (QOE) is implemented in an OTT optimization platform communicatively coupled to a plurality of devices in a network. The process includes receiving a plurality of inputs including network inputs, service and software inputs, and QOE inputs; analyzing the plurality of inputs with respect to one or more OTT content streams to perform an optimization thereof; determining adjustments in the network based on the optimization; and one of notifying a network operator of the adjustments and automatically causing the adjustments in the network. 
     The network can include a plurality of an optical layer, a packet layer, a compute layer, and a video functions layer, and wherein the adjustments can include changes to one or more of i) network elements in the optical layer, ii) network elements in the packet layer, iii) virtual network elements in the NFVI, and iv) compute resources in the video functions layer. The network inputs and the service and software inputs can be received from a management plane, and wherein the QOE data can be received from a database which manages the QOE data from players associated with user devices. The network can include a combination of OTT delivery elements to end users and OTT content publishing elements to provide the OTT content streams to the network, and wherein the optimization can include evaluating tradeoffs for configured of the OTT delivery elements and the OTT content publishing elements based off a plurality of factors including revenue generation, cost, resource use, function, risk, location, and impact on user QOE. 
     The receiving can be via one or more Application Programming Interfaces (APIs) from a management plane associated with the network or directly from the network itself. The QOE inputs can be related to one of an individual OTT content stream to a user, a selected representative OTT content stream to a selected user amongst a group of users associated with a corresponding group of OTT content streams, and an aggregate QOE for the group of OTT content streams, and wherein the adjustments can correspond to one of the individual OTT content stream and the group of OTT content streams. The adjustments can include a determination of caching in the network based on the OTT content to improve the QOE inputs. 
     The determining can include a determination of a particular file for OTT content is improperly encoded on a cache., and wherein the adjustments can include deletion of the file and an increase of bandwidth back to an origin server until caching of the file stabilizes. The method of claim  1 , wherein the optimization can include an evaluation of various tradeoffs to determine the adjustments, and wherein the evaluation can be based on the receiving, historical inputs, forecasted use, and user defined inputs. The tradeoffs can be based on QOE versus one or more of compute resources, bandwidth, latency, and encoding of the OTT content. The QOE data can be consolidated by a system receiving feedback from a plurality of players associated with user devices consuming the OTT content. 
     Optimization Examples 
     Again, the optimization is performed on a single OTT content stream or a group of OTT content streams. The following illustrate some non-limiting examples of the optimization performed by the optimization platform  40  and/or the OTT content optimization method  100 . For example, where the QOE data identifies n+ (where n is defined in policy) users impacting QOE issues, and all of the n+ users are sourced from caches located in the same location running at higher than normal CPU levels, the optimization platform  40  can spin up new instances of caches in that location along with the appropriate changes or instantiations of load balancers, firewalls, and routing/switching NFV functions to spread the high load amongst a greater number of caches. 
     Also, where QOE measurements identify n+ (where n is defined in policy) flows with user impacting issues, the optimization platform  40  can use the detailed flow information to map out the path that flow would traverse over the network topology and examine each of the network and compute components along the path. It could then identify network anomalies. For example, it might recognize unusually high latency for flows over a path and reoptimize the network to put the video flows on a lower latency path. For example, the optimization platform  40  might recognize unusual intermittent packet drop for flows over a network link and could perform various actions. 
     First, the various actions could include, where different networks (e.g., video, Internet, business) are overlaid on the same infrastructure with individual bandwidth quotas, the optimization platform  40  could adjust the video network to have a higher bandwidth quota. Second, where there is an open consumption model, the optimization would look at increasing the bandwidth quota, if possible. If not possible, it could example the network resources available to to add bandwidth to the network by creating new wavelengths, Link Aggregation Group (LAG) groups, Multiprotocol Protocol Label Switching (MPLS) Label Switched Paths (LSPs), etc. and their associated buffer, queuing, and like characteristics along the constrained path. 
     Third, where IP QOS is active or configurable, the optimization could adjust the discard eligibility and emission priorities for the video to be more favorable. Fourth, where MPLS is available, the optimization could use MPLS to traffic engineer the flow around the congestion point. The optimization platform  40  could also reroute users to a different cache, away from the congestion. The optimization platform  40  could analyze all of the aforementioned options and any other options that apply to select the least cost effective solution. 
     The optimization platform  40  could periodically assess trends in QOE. If the optimization platform  40  recognizes a decreasing user QOE, the optimization platform  40  could identify the portions of the network and compute infrastructure that are most commonly associated with the users experiencing a QOE decline and revector resources to increase the QOE (as discussed above) to proactively prevent the QoE from crossing a policy defined threshold for minimum Quality of Experience. On the other hand, the optimization platform  40  could similarly reduce the resources associated with video QOE where QOE is well beyond the QOE policy to reduce the overall service cost. The optimization platform  40  could identify that optimization platform  40  issues for n+ users and map to the videos at the point of ad insertions and provision the OVP to turn off ad insertion or to use a statically defined ad while operators investigate the dynamic ads for corruption or connection issues. 
     The optimization platform  40  could periodically assess the cost tradeoff of bandwidth vs. storage while maintaining a QOE above a policy directed threshold. Making a large quantity of storage available for caching in the network allows for more content to be cached and thus less bandwidth would be required from upstream content sources. Similarly, making a large quantity of bandwidth available reduces the need for caching all but the most frequently requested content and thus less storage is required. The optimization platform  40  could look at its policy for the cost of storage versus the cost of bandwidth and couple with that the cache hit rates of the content being cached. The optimization platform  40  may determine that it could save costs by freeing up storage and/or adding caching and reverse proxy instances and boosting the bandwidth resources to the end user or redirecting them to another caching site due to content having a low cache hit rate. Similarly, if the optimization platform  40  found a high cache hit rate, it may determine that adding additional storage to existing cache and/or increasing the cache instances and reducing the available bandwidth would be more effective and less costly. 
     Exemplary Server 
     Referring to  FIG. 5 , in an exemplary embodiment, a block diagram illustrates an exemplary implementation of a server  200 , which can be used to implement the optimization platform  40  and/or the OTT content optimization method  100 . The server  200  can be a digital processing device that, in terms of hardware architecture and functionality, generally includes a processor  202 , input/output (I/O) interfaces  204 , a network interface  206 , a data store  208 , and memory  210 . It should be appreciated by those of ordinary skill in the art that  FIG. 5  depicts the server  200  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 202 ,  204 ,  206 ,  208 , and  210 ) are communicatively coupled via a local interface  212 . The local interface  212  can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  212  can have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  212  can include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  202  is a hardware device for executing software instructions. The processor  202  can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the server  200 , a semiconductor-based microprocessor (in the form of a microchip or chip set), or generally any device for executing software instructions. When the server  200  is in operation, the processor  202  is configured to execute software stored within the memory  210 , to communicate data to and from the memory  210 , and to generally control operations of the server  200  pursuant to the software instructions. The I/O interfaces  204  can be used to receive user input from and/or for providing system output to one or more devices or components. The network interface  206  can be used to enable the server  200  to communicate on a network. 
     The data store  208  can be used to store data. The data store  208  can include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store  208  can incorporate electronic, magnetic, optical, and/or other types of storage media. The memory  210  can include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.), and combinations thereof. Moreover, the memory  210  can incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  210  can have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor  202 . The software in memory  210  can include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. The software in the memory  210  includes a suitable operating system (O/S)  214  and one or more programs  216 . The operating system  214  essentially controls the execution of other computer programs, such as the one or more programs  216 , and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The one or more programs  216  may be configured to implement the various processes, algorithms, methods, techniques, etc. described herein. 
     It will be appreciated that some exemplary embodiments described herein may include one or more generic or specialized processors (“one or more processors”) such as microprocessors; Central Processing Units (CPUs); Digital Signal Processors (DSPs): customized processors such as Network Processors (NPs) or Network Processing Units (NPUs), Graphics Processing Units (GPUs), or the like; Field Programmable Gate Arrays (FPGAs); and the like along with unique stored program instructions (including both software and firmware) for control thereof to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and/or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more Application Specific Integrated Circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic or circuitry. Of course, a combination of the aforementioned approaches may be used. For some of the exemplary embodiments described herein, a corresponding device in hardware and optionally with software, firmware, and a combination thereof can be referred to as “circuitry configured or adapted to,” “logic configured or adapted to,” etc. perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and/or analog signals as described herein for the various exemplary embodiments. 
     Moreover, some exemplary embodiments may include a non-transitory computer-readable storage medium having computer readable code stored thereon for programming a computer, server, appliance, device, processor, circuit, etc. each of which may include a processor to perform functions as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), Flash memory, and the like. When stored in the non-transitory computer readable medium, software can include instructions executable by a processor or device (e.g., any type of programmable circuitry or logic) that, in response to such execution, cause a processor or the device to perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. as described herein for the various exemplary embodiments. 
     Although the present disclosure has been illustrated and described herein with reference to preferred embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other embodiments and examples may perform similar functions and/or achieve like results. All such equivalent embodiments and examples are within the spirit and scope of the present disclosure, are contemplated thereby, and are intended to be covered by the following claims.