Media distribution management system and device

Abstract

A Unified Content Delivery Network system (UCDN) formed from one or more interoperable peer networks, comprising five main SPAN-AI subsystems: Unified Naming; Unified Discovery; Hybrid Adaptive Routing; Scalable Pub / Sub; and a hierarchical, hybrid, adaptive, secure peer-assisted networking system (referred to as SPAN-AI) that uses a hierarchical, AI-driven approach under a unified, secure content-addressable architecture based on embedded security; all five main SPAN-AI subsystems are securely integrated and co-optimized via a hierarchical, pluggable AI framework with associated simulation, training, and development pipelines incorporating AI agents with varying degrees of awareness and optimization capabilities at the peer, edge, core, or other network levels (tiers).

Description

[Technical Field]

[0001]

[0001] The present invention relates to media distribution management systems, and more particularly to such systems when utilizing line termination units (NTUs) or Internet appliances that interface with the Internet infrastructure to distribute and control digital content, including (but not limited to) streamed and downloaded digital content, to digital devices including (but not limited to) television display units, video display units, and the like. [Background technology]

[0002]

[0002] There are certain forms of content receiving and content viewing devices available to consumers. These include television "set-top boxes" provided by media distribution companies. Popular versions in Australia include Foxtel set-top boxes and Optus set-top boxes. These devices are typically limited to receiving and distributing content that is normally distributed via cable arrangements.

[0003]

[0003] Particular forms of "Internet appliances" are also known which receive digital content, typically over the internet, for delivery to television display devices and the like, typically via streaming. One example of such a device currently in use within Australia is the "Apple TV" appliance.

[0004]

[0004] It is also known to stream digital content to personal computers over the Internet with the aid of file sharing services such as BitTorrent. Such services and their protocols are highly unstable, not suitable for streaming, often take a long time to start playback, and are not suitable for features such as "jumping" to a specified point in the content.

[0005]

[0005] The problem with these current devices and mechanisms for receiving and delivering digital content is that the current Internet infrastructure has variable upload and download speeds, and it can be difficult, and in some cases impossible, for consumers, especially in residential environments, to reliably receive content on demand, especially high-resolution and ultra-high-resolution content or large file content, in real time or near real time.

[0006]

[0006] Many, if not all, current Internet video delivery systems use adaptive bitrate (ABR) technology to overcome the problem of video on demand delivery over the Internet. However, ABR reduces the bitrate and resolution, and degrades the user experience.

[0007]

[0007] Alternatively, and in some cases in addition, the selection of content available to consumers is limited by the proprietary value of the device.

[0008]

[0008] Furthermore, current mechanisms for local control of content and its distribution and display are not intuitive or "user-friendly."

[0009]

[0009] The Internet, and in particular the TCP / IP protocols and the routing protocols based on them, are reaching their limits. Video is placing a huge strain on the Internet that was unforeseen at the time of its invention.

[0010]

[0010] After decades of centralization in hyperscale data centers, networks are beginning to move back to the "edge," but along the way they are encountering some subtle and critical problems.

[0011]

[0011] The global Covid-19 pandemic has changed network usage patterns and loads overnight in ways that have lasted for decades.

[0012]

[0012] Emerging applications such as industrial automation, machine vision, AR, 5G and other future applications will place even more strain on global networks and the Internet.

[0013]

[0013] Global carriers, CDNs and ISPs are rushing to catch up, but no single network can solve these problems. A new approach is needed that can seamlessly interoperate and scale for the foreseeable future.

[0014]

[0014] The present invention aims to address or at least ameliorate some of the above-mentioned disadvantages, or to provide a useful alternative.

[0015] Note

[0015] The term "comprises" (and its grammatical variations) is used in this specification in the inclusive sense of "having" or "including" and not in the exclusive sense of "consisting only of."

[0016]

[0016] The above discussion of prior art in the background of the present invention is not intended to be an admission that the information discussed therein is part of the citable prior art or the common general knowledge of a person skilled in the art in any country. Summary of the Invention

[0017]

[0017] Thus, in one broad form, the present invention provides a unified content delivery network system (UCDN) formed from one or more interoperable peer networks.

[0018] Preferably, the peer network is a SPAN-AI network.

[0019]

[0019] Preferably, the system comprises five main SPAN-AI subsystems: unified naming; unified discovery; hybrid adaptive routing; scalable pub-sub; and a hierarchical hybrid adaptive AI-driven networking technology (called Secure Peer-Assisted Networking or SPAN-AI) that uses an AI-driven hybrid adaptive routing approach based on embedded security; all five main SPAN-AI subsystems are securely integrated and co-optimized via a hierarchical pluggable AI framework with associated simulation, training and development pipelines incorporating AI agents with varying degrees of awareness and optimization capabilities at peer, edge, core or other network levels (tiers).

[0020]

[0020] Preferably, the system uses an integrated naming and discovery (UND) system that i) maps human-readable, changeable names (e.g., domain names, content names) to immutable, self-authenticating CIDs by adding a name prefix to the beginning of each content identifier, and ii) enables routing of CIDs through both name resolution-based routing subsystems and name-based routing subsystems.

[0021]

[0021] In another preferred embodiment, the system uses an integrated naming and discovery (UND) system that i) maps human-readable, changeable names (e.g., domain names, content names) to immutable, self-authenticating CIDs, and ii) enables routing of CIDs through both name resolution-based routing subsystems and name-based routing subsystems, by combining names and content identifiers in a manner that optimizes routing and / or storage.

[0022] Preferably, UND also incorporates IP DNS to ensure backward compatibility.

[0023]

[0023] Preferably, the system further employs an AI-driven universal discovery system, including a key component, Ambient Intelligent Rendezvous (referred to as AmI Rendezvous), that provides smart discovery, configuration and self-organization services.

[0024]

[0024] Preferably, the SPAN-AI system supports large-scale routing through an AI-driven hybrid adaptive routing design (referred to as the AI-HARD system); the AI-HARD system is composed of two subsystems: a storage-centric routing subsystem; and a delivery-centric routing subsystem; the subsystems combine the benefits of name resolution-based routing (NRR) for scalable, available, and accessible distributed storage with the advantages of name-based routing (NBR) for fast and reliable content delivery.

[0025]

[0025] Preferably, AI HARD also incorporates IP routing to provide backward compatibility.

[0026] Preferably, the AI-HARD intelligent agent within SPAN-AI leverages predictive knowledge of network conditions and application requirements to adaptively select the most efficient routing policy from the subsystems.

[0027] Preferably, the system provides backward compatibility with both SPAN-AI's smart discovery service AmI Rendezvous and IP Name Discovery, or DNS.

[0028]

[0028] Preferably, AI-HARD protocols, naming standards, rules and methods are published to enable incorporation into existing and new routers, thereby enabling interoperability between existing IP networks and new SPAN-AI networks.

[0029] Preferably, the protocols, naming standards, rules and methods include IP naming.

[0030] Preferably, the AI-HARD system interoperates with multiple storage and distribution networks.

[0031]

[0031] Preferably, the storage and distribution network may operate with cryptographic tokens such as Filecoin or Blast.

[0032] Preferably, the SPAN-AI system utilizes an AI-driven Pub-Subsystem for asynchronous multi-party distribution services supporting control plane distribution of directory updates (names, discovery, configuration) and intelligence updates (optimization / control operations); and data plane distribution of collaborative applications such as social networks, video conferencing, etc.

[0033] Preferably, SPAN-AI uses an AI-driven pub-subsystem for asynchronous multi-party distribution services, including communication between AI agents, naming services, and discovery services.

[0034]

[0034] Preferably, the AI-driven pub-subsystem includes interoperability with an IP discovery service.

[0035]

[0035] Preferably, the pub / subsystem uses an AmI rendezvous service enhanced with peer heartbeats and mesh health metrics and rankings to improve operations, intelligent discovery and configuration through a combination of awareness and control for peer / local intelligence; edge / swarm intelligence; and core / global intelligence.

[0036] Preferably, AmI Rendezvous incorporates a pluggable interface for self-healing agents that incorporate AmI Rendezvous clients into the pub / sub protocol, for example an evolution of existing pub-sub algorithms such as Gossipsub, PlumTree, and HyParView.

[0037] Preferably, the SPAN-AI system incorporates integrated security at all levels.

[0038]

[0038] Preferably, the SPAN-AI system uses machine learning and recognition to detect and manage security threats.

[0039]

[0039] Preferably, the content is encrypted using a DRM system such as PlayReady before being published to the system.

[0040]

[0040] Preferably, the data packet is cryptographically signed by the issuer.

[0041]

[0041] Preferably, naming is rooted in self-sovereign identity, which can be defined as a lifelong, portable digital identity that is independent of any centralized authority.

[0042]

[0042] Preferably, the system uses decentralized identifiers that provide persistence, global resolvability, cryptographic verifiability and decentralization.

[0043]

[0043] Preferably, the name is self-authenticating.

[0044]

[0044] Preferably, the system is based on a hardware root of trust and secure boot.

[0045]

[0045] Preferably, the system utilizes the Web of Trust methodology.

[0046]

[0046] Preferably, the system utilizes quantum cryptography, ie, cryptography based on quantum state random number generators.

[0047]

[0047] Preferably, the system coordinates adaptive operation of routing and pub / subsystems through a family of pluggable hierarchical (local / edge / global / other) AI agents that provide monitoring, prediction, optimization and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.

[0048]

[0048] Preferably, the system provides a method for pluggable AI agents that enable open and flexible innovation in universal network optimization and control.

[0049]

[0049] Preferably, the AI ​​agents are exchangeable for cryptographic tokens such as Filecoin or Blast.

[0050]

[0050] Preferably, the SPAN-AI system uses a simulation, training and development pipeline that enables cloud-level replication of AI model and agent runtime environments, simulation, testing and training, pluggable to peer / edge / core / other network nodes for real-time optimization and control.

[0051]

[0051] Preferably, the system further includes a self-aware mesh simulator (referred to as the SAMSim system), supported by a distributed cloud hosting a big data lake of meshes with health metrics for simulating and deploying AI models across an automated software engineering pipeline.

[0052]

[0052] According to another broad aspect of the present invention, there is provided a hierarchical hybrid adaptive secure peer-assisted networking system (referred to as SPAN-AI) that uses a hierarchical AI-driven approach under an integrated secure content-addressable architecture based on five main SPAN-AI subsystems: integrated naming; integrated discovery; hybrid adaptive routing; scalable pub / sub; and embedded security; all of the five main SPAN-AI subsystems are securely integrated and co-optimized via a hierarchical pluggable AI framework with associated simulation, training and development pipelines incorporating AI agents with varying degrees of awareness and optimization capabilities at the peer, edge or core or other network levels (tiers).

[0053]

[0053] Preferably, the system uses an integrated naming and discovery (UND) system that i) maps human-readable, changeable names (e.g., domain names, content names) to immutable, self-authenticating CIDs by iii) adding a name prefix to the beginning of each content identifier, and ii) enables routing of CIDs through both name resolution-based routing subsystems and name-based routing subsystems.

[0054]

[0054] In another preferred embodiment, the system uses an integrated naming and discovery (UND) system that i) maps human-readable, changeable names (e.g., domain names, content names) to immutable, self-authenticating CIDs, and ii) enables routing of CIDs through both name resolution-based routing subsystems and name-based routing subsystems, by combining names and content identifiers in a way that optimizes routing and / or storage.

[0055]

[0055] Preferably, the system further employs an AI-driven integrated discovery system including Ambient Intelligent Rendezvous (referred to as AmI Rendezvous), a key component that provides smart discovery, configuration and self-organization services.

[0056]

[0056] Preferably, the SPAN-AI system supports large-scale routing through an AI-driven hybrid adaptive routing design (referred to as the AI-HARD system); the AI-HARD system is composed of two subsystems: a storage-centric routing subsystem; and a delivery-centric routing subsystem; the subsystems combine the benefits of name resolution-based routing (NRR) for scalable, available, and accessible distributed storage with the advantages of name-based routing (NBR) for fast and reliable content delivery.

[0057] Preferably, the AI-HARD intelligent agent within SPAN-AI leverages predictive knowledge of network conditions and application requirements to adaptively select the most efficient routing policy from the subsystems.

[0058] Preferably, the AI-HARD protocol is published to allow incorporation into existing and new routers, thereby ensuring routing compatibility between all networks.

[0059] Preferably, the AI-HARD system interoperates with multiple storage and distribution networks.

[0060]

[0060] Preferably, the storage and distribution network may operate with cryptographic tokens such as Filecoin or Blast.

[0061]

[0061] Preferably, the SPAN-AI system utilizes an AI-driven pub-subsystem for asynchronous multi-party distribution services supporting control plane distribution of directory updates (names, discovery, configuration) and intelligence updates (optimization / control operations); and data plane distribution for collaborative applications, e.g., for video conferencing, social networking, etc.

[0062]

[0062] Preferably, SPAN-AI uses an AI-driven pub-subsystem for asynchronous multi-party distribution services, including communication between AI agents, naming services, and discovery services.

[0063]

[0063] Preferably, the pub / subsystem uses an AmI rendezvous service enhanced with peer heartbeats and mesh health metrics and rankings to improve operations, intelligent discovery and configuration through a combination of awareness and control for peer / local intelligence; edge / swarm intelligence; core / global and other intelligence.

[0064]

[0064] Preferably, AmI Rendezvous incorporates a pluggable interface for self-healing agents that incorporate AmI Rendezvous clients into pub / sub protocols, for example evolution of existing pub-sub algorithms such as Gossipsub, PlumTree, HyParView, etc.

[0065]

[0065] Preferably, the SPAN-AI system incorporates integrated security at all levels.

[0066]

[0066] Preferably, the SPAN-AI system uses machine learning and recognition to detect and manage security threats.

[0067]

[0067] Preferably, the content is encrypted using a DRM system such as PlayReady before being published to the system.

[0068]

[0068] Preferably, the data packet is cryptographically signed by the issuer.

[0069]

[0069] Preferably, naming is rooted in self-sovereign identity, which can be defined as a lifelong, portable digital identity that is independent of any centralized authority.

[0070]

[0070] Preferably, the system uses decentralized identifiers that provide persistence, global resolvability, cryptographic verifiability and decentralization.

[0071]

[0071] Preferably, the name is self-authenticating.

[0072]

[0072] Preferably, the system is based on a hardware root of trust and secure boot.

[0073]

[0073] Preferably, the system utilizes the Web of Trust methodology.

[0074]

[0074] Preferably, the system utilizes quantum cryptography, ie, cryptography based on quantum state random number generators.

[0075]

[0075] Preferably, the SPAN-AI system coordinates the adaptive operation of routing and pub / subsystems through a family of pluggable hierarchical (local / edge / global / other) AI agents that provide monitoring, prediction, optimization and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.

[0076]

[0076] Preferably, the system provides a method for pluggable AI agents that enable open and flexible innovation in universal network optimization and control.

[0077]

[0077] Preferably, the AI ​​agents are exchangeable for cryptographic tokens such as Filecoin or Blast.

[0078]

[0078] Preferably, the SPAN-AI system uses a simulation, training and development pipeline that enables cloud-level replication of AI model and agent runtime environments, simulation, testing and training, pluggable to peer / edge / core / other network nodes for real-time optimization and control.

[0079]

[0079] Preferably, the system further includes a self-aware mesh simulator (referred to as the SAMSim system), supported by a distributed cloud hosting a big data lake of meshes with health metrics for simulating and deploying AI models across an automated software engineering pipeline.

[0080]

[0080] According to another broad aspect of the present invention, there is provided a hierarchical hybrid adaptive secure peer-assisted networking system (referred to as SPAN-AI) that uses a hierarchical AI-driven approach under an integrated secure content addressable architecture; the system comprises large-scale routing via an AI-driven hybrid adaptive routing design (referred to as AI-HARD system) consisting of two subsystems: a storage-centric routing subsystem; and a delivery-centric routing subsystem; the two subsystems combine the benefits of name resolution-based routing (NRR) for scalable, available, and accessible distributed storage with the advantages of name-based routing (NBR) for fast and reliable content delivery.

[0081]

[0081] Preferably, the AI-HARD system interoperates with multiple storage and distribution networks.

[0082]

[0082] Thus, in another broad form, the present invention provides SPAN-AI for AI-driven secure peer-assisted networking, a hybrid adaptive networking technology that provides globally scalable, secure, distributed content storage, computation, and delivery for any application and network environment. SPAN-AI recognizes the limitations of existing technologies that are only suitable for specific applications on a non-global scale, and uses an AI-driven hybrid routing approach to improve and adaptively combine the best features of existing solutions under a unified, secure, content-addressable architecture. We call this the Unified Content Delivery Network, or UCDN. SPAN-AI is based on five key systems: unified naming; unified discovery; hybrid routing; scalable pub-sub; and embedded security; all securely integrated and co-optimized via a hierarchical, pluggable AI framework with associated simulation, training, and development pipelines incorporating AI agents with varying degrees of awareness and optimization capabilities at the peer, edge, core, and other network levels.

[0083]

[0083] Preferably, SPAN-AI uses a unified naming system that i) maps human-readable, changeable names (e.g., domain names, content names) to immutable, self-authenticating CIDs by adding a name prefix to the beginning of each content identifier, and ii) enables routing of CIDs through both the name resolution-based routing subsystem and the name-based routing subsystem.

[0084]

[0084] In another preferred embodiment, SPAN-AI uses a unified naming system that i) maps human-readable, changeable names (e.g., domain names, content names) to immutable, self-authenticating CIDs, and ii) enables routing of CIDs through both name resolution-based routing subsystems and name-based routing subsystems, by combining name prefixes and content identifiers in a manner that optimizes routing and / or storage.

[0085]

[0085]

[0086] Preferably, SPAN-AI uses an integrated discovery system based on the Ambient Intelligent Rendezvous Service, AmI Rendezvous, designed to provide smart discovery and self-organization services through a combination of hierarchical AI awareness and control agents; peer / local intelligence; edge / swarm intelligence; core / global intelligence and other levels of intelligence. AmI Rendezvous includes peer heartbeat collection, mesh health metrics aggregation, peer ranking, peer discovery and mesh self-configuration services.

[0087]

[0087] Preferably, SPAN-AI is composed of two subsystems: a storage-centric routing subsystem; and a distribution-centric routing subsystem; and addresses large-scale routing via an AI-driven hybrid adaptive routing design (AI-HARD) that aims to combine the benefits of Name Resolution-Based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-Based Routing (NBR) for fast and reliable content delivery. AI-HARD uses hierarchical AI agents to control and optimize the integrated operation of the NRR and NBR subsystems. AI-Hard can use AmI rendezvous for discovery and self-organization in highly dynamic scenarios. AI-HARD includes storage and distribution markets.

[0088] Preferably, SPAN-AI uses an AI-driven publish / subscribe (pub-sub) system for asynchronous multi-party distribution services supporting: control plane distribution of directory updates (names, discovery, configuration) and intelligence updates (optimization / control operations); and data plane distribution of collaborative applications, e.g., for video conferencing, social networks, etc. SPAN-AI pub-sub uses AmI rendezvous for pub-sub mesh discovery and self-organization, including a pluggable interface for self-healing agents to the pub / sub protocol, which is an evolution of existing pub-sub algorithms such as Gossipsub, PlumTree, and HyParView.

[0089]

[0089] Preferably, SPAN-AI incorporates integrated security at all levels. SPAN-AI uses machine learning and recognition to detect and manage security threats. Content can be encrypted using commercial DRM systems such as PlayReady before being published to the system. Data packets can be cryptographically signed by the publisher. Naming is rooted in self-sovereign identity, which can be defined as a lifelong, portable digital identity that does not rely on any centralized authority. Naming uses decentralized identifiers that provide persistence, global resolvability, cryptographic verifiability, and decentralization. Names can be self-authenticating. Preferred embodiments are based on a hardware root of trust and secure boot. Further preferred embodiments may utilize web of trust methods. Quantum cryptography, i.e., encryption based on quantum state random number generators, may be used.

[0090]

[0090] Preferably, SPAN-AI coordinates routing and adaptive operation of pubs / subsystems through a family of pluggable hierarchical (local / edge / global) AI agents that provide monitoring, prediction, optimization and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.

[0091] Preferably, SPAN-AI provides a marketplace for pluggable AI agents that enable open and flexible innovation in the optimization and control of integrated networks, which may be based on crypto tokens such as Filecoin or Blast.

[0092]

[0092] Preferably, SPAN-AI uses a simulation, training and development pipeline that enables cloud-level replication, simulation, testing and training of runtime environments for AI models and agents that can be plugged into peer / edge / core / other network nodes for real-time optimization and control.

[0093]

[0093] Preferably, SPAN-AI includes a simulation pipeline, a Self-Aware Mesh Simulator (SAMSim), supported by a distributed cloud hosting a big data lake of meshes with health metrics to simulate and deploy AI models across an automated software engineering pipeline.

[0094]

[0094] Thus, in another broad form of the present invention, there is provided a network device for receiving digital content from a remote location; the device including decoding and re-encoding means by which the digital content is downloaded, decoded and then re-encoded for transmission to a digital device for consumption by a user.

[0095]

[0095] Preferably, the digital content is re-encoded according to a secure HDMI encoding algorithm.

[0096]

[0096] Preferably, the network device is a. Most needed packets b. Fastest download speeds c.Minimum latency d. a network address from which the next digital bit or group of bits can be most easily and efficiently obtained in order to maintain real-time or near real-time delivery of the digital content.

[0097]

[0097] In a broader form, the present invention provides a web server that aggregates items of digital content in response to requests from remotely located network equipment for subsequent transfer of at least a portion of a copy of the item in accordance with a secure methodology.

[0098]

[0098] Preferably, said secure methodology meets the following criteria: a. Most needed packets b. Fastest download speeds c.Minimum latency d. To maintain real-time or near real-time delivery of digital content, the method includes obtaining and forwarding packets of data forming said digital content according to one or more of: a network address from which the next digital bit or group of bits can be most easily and efficiently obtained.

[0099]

[0099] In a broader form of the present invention, there is provided a method for assembling an item of digital content; the method includes receiving at least a first portion of the item of digital content from a remotely located origin store of the digital content.

[0100]

[0100] Preferably, the method further comprises the following criteria: a. Most needed packets b. Fastest download speeds c.Minimum latency d. a network address from which the next digital bit or group of bits can be most easily and efficiently obtained, in order to maintain real-time or near real-time delivery of the digital content.

[0101]

[0101] In a broader form of the present invention, there is provided a distributed system for distribution of digital content; the system comprising at least one content aggregator in communication with an origin store; a plurality of network devices; the aggregator receives digital content in the form of items of content; the aggregator protects the digital content for distribution by the system; the origin store makes the digital content available to the plurality of network devices; and each network device receives an identified item of content in response to a request from the network device to the system.

[0102]

[0102] Preferably, the system communicates over the Internet.

[0103]

[0103] Preferably, each network device operates according to a secure peer-assisted standard; said secure peer-assisted standard enabling receipt of at least a portion of said item of content from another of said plurality of network devices if said item of content has previously been downloaded to said other of said plurality of network devices.

[0104]

[0104] In a broader form of the present invention, there is provided a system for capturing, collecting, curating, managing, publishing, searching, selling, distributing and settling purchases of digital content; said system operating in accordance with the method described above.

[0105]

[0105] Preferably, the step of settling involves paying content owners and retailers for specified items of digital content in accordance with complex rights and release time agreements.

[0106]

[0106] In a broader form, the present invention provides a method of syndicating the above-described system, thereby enabling multiple Internet retailers to sell digital content transmitted in accordance with the above-described method.

[0107]

[0107] It is understood that each of the following figures is a representation of a particular aspect of the present invention and is not intended to be comprehensive or complete by itself or together. In particular, in the system or block diagrams, it is understood that any system or subsystem may be functionally, logically, or physically connected to or through any other system or subsystem, with or without variations in the connections. [Brief explanation of the drawings]

[0108]

[0108] Embodiments of the present invention will now be described with reference to the accompanying drawings.

[0109] [Figure 1]

[0109] This is a block diagram of a system that combines and expands subsystems to form a broadly applicable, universally operable, highly scalable, and efficient system for optimizing, managing, and operating a Unified Content Delivery Network (UCDN) incorporating AI-driven Secure Peer-Assisted Networking (SPAN-AI). SPAN-AI is a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment. SPAN-AI recognizes the limitations of existing technologies that are only suitable for specific applications on a non-global scale, and uses an AI-driven approach to improve and adaptively combine the best capabilities of existing solutions under a unified, secure, content-addressable architecture. We call this the Unified Content Delivery Network, or UCDN. [Figure 1A]

[0110] FIG. 2 is a system block diagram of a SPAN-AI system incorporating AI-HARD that can be operated in the context of the UCDN system of FIG. 1 . [Figure 1B]

[0111] FIG. 1 is a block diagram of one embodiment of a UCDN, which is one of one or more SPAN-AI networks operating through an AI, routing, or other interface. [Figure 1C]

[0112] 1 is a further embodiment of the UCDN incorporating a legacy network, which may be a TCP / IP or other protocol network. [Figure 2A]

[0113] 1B is a block diagram of a media distribution management system including the SPAN-AI components of the first embodiment and of FIG. 1A, as a further embodiment of the present invention (the SPAN-AI embodiment). [Figure 2B]

[0114] FIG. 2B is a block diagram of information flow through the network of FIG. 2A when publishing to a Name Resolution Routing (NRR) system. [Figure 2C] [001151] FIG. 2B is a block diagram of information flow through the network of FIG. 2A via a name-based routing (NBR) system. [Figure 3] FIG. 2B is a block diagram of network equipment usable with the system and associated routing method of FIG. 2A. [Figure 3A] FIG. 1B is a block diagram of a routing table used in the SPAN-AI and AI HARD configurations of FIG. 1A. [Figure 4]

[0118] FIG. 4 is a video output view showing the graphical structure utilized interactively in the operational control mode of the device of FIG. 3; [Figure 5A]

[0119] FIG. 4 is a video output view showing further graphical structures that are utilized interactively in the operational control mode of the equipment of FIG. 3. [Figure 5B]

[0119] FIG. 4 is a video output view showing further graphical structures that are utilized interactively in the operational control mode of the equipment of FIG. 3. [Figure 5C]

[0119] FIG. 4 is a video output view showing further graphical structures that are utilized interactively in the operational control mode of the equipment of FIG. 3. [Figure 5D]

[0119] FIG. 4 is a video output view showing further graphical structures that are utilized interactively in the operational control mode of the equipment of FIG. 3. [Figure 5E]

[0119] FIG. 4 is a video output view showing further graphical structures that are utilized interactively in the operational control mode of the equipment of FIG. 3. [Figure 5F]

[0119] FIG. 4 is a video output view showing further graphical structures that are utilized interactively in the operational control mode of the equipment of FIG. 3. [Figure 6]

[0120] A block diagram of a media distribution management system according to one implementation example. [Figure 7]

[0121] FIG. 7 is a block diagram of an aggregator of the system of FIG. 6. [Figure 8]

[0122] Figure 7 is a flow chart of service functions affecting the aggregator and origin store of the system of Figure 6; [Figure 9]

[0123] FIG. 7 is a flow diagram of data packet sources and data packet flows that may contribute in whole or in part to the delivery of digital content under the system of FIG. 6. [Figure 10]

[0124] A conceptual diagram of the overall system of Figure 6 including the methodology for syndication. [Figure 11]

[0125] FIG. 2B is a block diagram of one embodiment of the system of FIG. 2A conceptualized from a user's perspective. [Figure 12]

[0126] A layout diagram of a processor module according to a further embodiment of the present invention. [Figure 13]

[0127] Figure 13 shows diagrammatically some of the module functions of the modules of Figure 12; [Figure 14]

[0128] FIG. 13 is a block diagram of a data function operable in a further embodiment of FIG. [Figure 15]

[0129] A screenshot of a menu screen output from the embodiment of Figure 12. [Figure 16]

[0130] A screenshot of a menu selection screen output from the embodiment of Figure 12. [Figure 17]

[0131] A screenshot of the selection screen interface of the embodiment of Figure 12. DETAILED DESCRIPTION OF THE INVENTION

[0110] First Preferred Embodiment

[0132] Referring to FIG. 2A, a block diagram of a media delivery management system 10 according to a first preferred embodiment is shown (also incorporating SPAN-AI system components that define a further embodiment—the SPAN-AI embodiment; see further description of the SPAN-AI embodiment in the specification).

[0111]

[0133] In this case, the system 10 includes an origin store 11 (sometimes referred to in some parts of this specification as a "super PoP"). The origin store 11 may be implemented as a single server or may itself be a network of servers. In certain commercial implementations, the server may form part of a content distribution network of commercial partners. The origin store 11 communicates with various databases 12 that contain digital content 13 available for licensed use (usually, but not always, subject to negotiation of appropriate terms). The origin store 11 typically receives the digital content 13 as "wrapped" content, meaning that the content has digital rights management (DRM) applied to it.

[0112]

[0134] The origin store 11 makes this content 13 available to subscribers or purchasers through network devices 14. The network devices 14 are located at or near the point of consumption of the digital content 13.

[0113]

[0135] According to embodiments of the present invention, network device 14 may receive digital content 13 directly from origin store 11 according to a communication protocol 15 commonly available when communicating over the Internet 16. Most commonly, communication will occur over the Internet 16, although other structures that facilitate the use of protocol 15 may be contemplated. Digital content 13 may be secured from the point of ingestion to network device 14 using one or more of the following security techniques and features: -Secure ingestion of the platform into a secure environment via Aspera -All master asset storage and processing remains within the platform's approved secure environment i. Mezzanine storage within the platform secure storage ii. Transcoding on the platform's secure transcoding farm iii. Platform DRM wrapping with secure Microsoft PlayReady servers iv. Transfer of DRM-wrapped assets from the platform to the EdgeCast origin store

[11] Another embodiment includes separate secure ingest, mezzanine storage, transcoding and DRM wrapping on and within a Motion Picture Association of America approved facility. -All rights and entitlements governed by the platform and Microsoft PlayReady -Delivery to network equipment hardware Trusted Execution Environments (TEEs) using secure PlayReady clients over Super PoPs and Secure Peer-Assisted™ EdgeCast networks -The system is designed from the ground up for multi-layered security -Network equipment protects DRM transcoding with TEE to secure HDMI HDCP connection to TV Network device network access is protected by Public Key Infrastructure (PKI) security and certificates.

[0114]

[0136] The communication structures and algorithms programmed into the aggregator database 12 and the network devices 14 are such that the content 13 is initially obtained from the aggregator database 12, typically over the Internet 16, following an initialization sequence that authorizes access to and use of 13 identified items 17 of digital content by a given network device 14. Again, although not always, authorization is subject to negotiation of terms and conditions before access to the identified items 17 is provided.

[0115]

[0137] Once all or a portion of the identified item 17 has been downloaded to a given network device 14, all or a portion of it can be "played" by that device. In most cases, the device outputs secure, e.g., HDMI HDCP, digital content to an audiovisual display device 18, such as a television set. In other embodiments, the identified item 17 can be securely streamed wirelessly or via Ethernet to other devices, such as tablets and phones and televisions. In other cases, the identified item 17 may be gaming content that is played on the device or "sideloaded" wirelessly, via Ethernet, or otherwise, onto a gaming console, such as another gaming platform.

[0116]

[0138] A feature of the present system 10 is that if another network device 14 negotiates and requests access to the same identified item 17, the content (or a portion thereof) may be downloaded from either the origin store 11 or the network device 14 that already has the identified item 17 stored on it.

[0117]

[0139] The decision on which sources to use is based on network knowledge and secure peer assistance criteria19, which include: a. Most needed packets b. Fastest download speeds c.Minimum latency d. A network address from which the next digital bit or group of bits can be most easily and efficiently obtained to maintain real-time or near real-time delivery of digital content.

[0118]

[0140] The routing information may be distributed and / or centralized and may be in the form of a hash table or other efficient database mechanism. This detailed knowledge combined with control of network devices 14 and routing is a form of software-defined networking (SDN).

[0119]

[0141] In particular, "network knowledge" includes address information for all data packets forming the digital content 13, and more particularly for all data packets that at any one time form part of an identified item 17. This data packet address information may be stored in a database 40 as shown in Figure 3, where each individual address, e.g. address AA of each data packet 24, is linked to a location, in this example location loc1.

[0120]

[0142] The database 40 may be stored in the origin store 11, or may form part of the origin store 11, or may be a separate server. In other examples, the database 40 may be stored at least partially in the memory 21 of each network device 14 to provide a distributed storage arrangement. It will be appreciated that over time, a significant number of widely distributed sources will become available from which the identified items 17 may be downloaded (in whole or in part).

[0121]

[0143] The decision on which sources to use may be made in cooperation with telephone companies and ISPs to optimize the use of their networks and minimize costs to consumers, telephone companies and ISPs and service operators. This may take the form of "unmetered content" agreements for secure peer-assisted traffic that remains within the network operator's domain.

[0122]

[0144] Different commercial models may be used for the basis on which identified items are permitted to be downloaded or streamed to a particular network device 14. For example, the model may be based on "pay-per-view" or ownership by rental or download.

[0123]

[0145] Alternatively, the model may be based on a subscription model.

[0124]

[0146] An example of a network appliance 14 is described in more detail below, but it should be understood that a processor programmed to provide the functionality described above could be located within a smartphone or a smart TV or a game controller—without needing to be limited to a particular standalone, dedicated network appliance 14.

[0125]

[0147] The combination of Super PoP CDN and secure peer-assisted standards ensures optimal delivery. Video packets are sourced from the best available location. A network of network equipment nodes provides an optimal network architecture: intelligence and storage at the farthest edge of the network, i.e., at the customer premises. This is reinforced by a Master Super PoP bridging the gap. This architecture ensures that user connections are driven at maximum bandwidth while minimizing hierarchical network traffic and inter-network peering. Network protocols and parameters are empirically optimized.

[0126]

[0148] Secure Peer Assist standards and application programs based on them recognize and report network traffic at the SCTP, TCP / IP, UDP, and video packet levels. Each network device 14 forms an intelligent node in a mesh network, sometimes described as grid computing or distributed cloud computing. We combine distributed and centralized routing information and intelligence down to the video packet level. This enables optimal management of the network with software-defined network-like performance.

[0127]

[0149] The Secure Peer Assist standard enables the creation of an entire ecosystem for managing video and game distribution over the Internet. Each network device 14 monitors metrics and statistics at the network and video packet level, reporting traffic and video status in real time. Combining a video asset management and distribution platform with a Super PoP CDN provides comprehensive quality of service (QoS) monitoring and control across the network. The Secure Peer Assist standard provides a highly efficient method of video distribution over the Internet, minimizing network load and maximizing network and customer viewing performance. The Secure Peer Assist standard may also be implemented in consumer electronics (CE) apps.

[0128]

[0150] The Secure Peer Assisted Standard 19 extends the reach of the network beyond the edge directly to the customer's home. The Secure Peer Assisted Standard 19 may be designed to take advantage of moderately high customer premises tail speeds using fiber backhaul from modern Internet exchanges. The Secure Peer Assisted Standard architecture uses a network of network equipment nodes, each programmed with the Secure Peer Assisted Standard 19, combined with a Super PoP CDN architecture to drive user connections at maximum capacity, thereby ensuring that content is delivered at the highest quality without any noticeable interruptions.

[0129]

[0151] In a preferred form, the digital content 13 stored in the origin store 11 may be syndicated. For example, the stored digital content 13 may be served as a store portal on someone's website, much like YouTube places a portal on its website. Participating site owners may select sub-catalogs of titles from the master catalog that are relevant to their audience.

[0130]

[0152] The aggregator database 12 may include the following techniques to assist in applying appropriate security to the digital content 13 before delivery to the origin store 11: -Designed from the ground up for multi-layered security -Secure peer-assisted networks are designed to be secure, hidden, and undetectable. -Secure peer-assisted network management system protected by PKI and secure certificates -Secure Peer Assist is "invisible" to the BitTorrent network and does not resemble such a network in its protocol. -All secure peer-assisted protocols are standard Internet protocols or secure protocols with PKI security and validation -All digital content13 is encrypted with Microsoft PlayReady DRM and protected within the network equipment TEE. -PlayReady DRM is implemented in the device hardware within its Trusted Execution Environment (TEE). -The device operating system is fully integrated with and utilizes hardware DRM to protect the media pipeline. In one embodiment, the device operating system may be Microsoft Windows -PlayReady key management is completely separate from and additional to network device TEE security and key management -PlayReady DRM and decryption are protected by network equipment TEE -Key management and storage is performed within a secure application and environment on the device In one embodiment, the secure key management system may utilize an innovative secure enclave environment enabled by a processor architecture, instruction set, libraries, application programming interfaces (APIs), and authentication services.

[0131] User Interface

[0153] Referring to FIG. 3, the network appliance 14 and an exemplary image display device 18 are shown in greater detail.

[0132]

[0154] In this example, network appliance 14 includes a processor or microprocessor 20 in communication with memory 21. Microprocessor 20 communicates with input / output devices 22 capable of sending and receiving signals from external digital devices, preferably including at least a visual display 23. The processor or microprocessor may include a graphics processing unit (GPU), or the GPU may be a separate processor, system, or subsystem.

[0133]

[0155] The memory contains code, including code corresponding to the Secure Peer Assist Standard 19, that enables the processor 20 to perform various functions, including sending and receiving digital content 13 over a network 25. The network 25 includes the Internet 16, a local area network 26, and a wide area network 27, all of which intercommunicate with each other.

[0134]

[0156] The digital content 13 typically comprises a number of data packets 24, each comprising a header 24A and a payload 24B.

[0135]

[0157] Payload 24B more specifically comprises digital data, which may be audio data, video data, game data, or other data.

[0136]

[0158] It should be noted that packets 24 do not necessarily arrive at device 14 in consecutive order: in a typical scenario, different packets arrive from different sources - in this regard, see Figures 9 and 11.

[0137]

[0159] The core function of network appliance 14 is to controllably send and receive digital content 13 and locally convert that digital content 13 into local signals 27 to drive external digital devices such as (but not limited to) audiovisual display apparatus 18.

[0138]

[0160] A further function of network device 14 is to allow users to control the “purchase” and “playback” of digital content received by or transmitted from network device 14 .

[0139]

[0161] In a preferred form, the user experience and user interface are kept as simple as possible. In its simplest form, user control is accomplished by simply moving a cursor left or right via a remote control device. These actions control very simple menus and the display of content on the screen. These can be homogenous or mixed, i.e., pure menus or pure content displays or a mix of both. In one preferred form, the display is an arc or circle that reflects the user experience and control via the remote control device. When there are many items to display, such as a large content library, the display can be concentric arcs or circles of content "tiles," i.e., clean graphic images of the "covers" of the content titles. In another embodiment, these tiles can be in a grid structure.

[0140]

[0162] Menu navigation is achieved through a simple combination of "left" and "right" navigation. In the simplest case, a menu of action items may be navigated left or right by clicking left or right. In one example, the menu may move correspondingly left or right under a selection graphic device such as a cursor box. In another example, the selection graphic device may move left or right. Once highlighted, a menu item is selected with a single click. This may cause an action or navigate deeper into the menu structure. Navigation "out" may be by double-clicking. Alternatively, there may be menu navigation items such as "back" or "cancel." When navigating multiple objects, such as a video library, these may be displayed as a grid of concentric arcs, rings, or tiles. Rings may be navigated "in" by clicking, "out" by double-clicking, or left or right by clicking left or right. The selected item, tile, arc, or ring may be highlighted by increasing its focus and / or size. Items, tiles, arcs, or rings that are not currently selected are moved out of focus by moving them from the center of focus and / or "defocusing" the items or by reducing their size, which may have the effect of the unselected items, tiles, arcs, or rings moving "away from" the user and the selected items, arcs, or rings moving "toward" the user.

[0141]

[0163] More sophisticated uses may be supported by control mechanisms such as speed or distance dependent actions. A small action may cause a menu or item to move slowly and briefly. A larger action may cause a menu or item to move faster and longer. Similarly, the speed of the action may also determine the magnitude or nature of the menu action. This may be independent of or related to the distance of the action.

[0142]

[0164] In the preferred form, the user graphical display is very simple, clean, uncluttered and crisp, providing a sense of simplicity and ease of use.

[0143]

[0165] For example, with reference to Figures 5A-5F, the sequence of operations may be as follows: graphic structures 28 on a substantially vertically arranged arc are shown in Figure 5A, or on a substantially horizontally arranged arc are shown in Figure 5B. The user manipulates the cursor 29 device to encircle a selected one of the graphic structures 28, for example, to designate the "My Movies" graphic structure.

[0144]

[0166] The user then moves the cursor through a series of movie selections, in this example, to designate the "Captain America" ​​movie selection as shown in Figure 5D.

[0145]

[0167] At any time, the user may "back out" of the current menu item and move up one level to the series of graphic structures 28 shown in Figure 5E. Figure 5F shows details of a particular selection when the "Captain America" ​​graphic structure is shown highlighted by cursor 29 (see Figures 5D-6 for purchase menus in store context available from syndicated web store 41).

[0146]

[0168] Alternatively, this may be done by controlling a cursor 29 in the form of a rectangular bounding device associated with a graphic structure 28 displayed on the image display 23, in this example the audiovisual display device 18.

[0147]

[0169] In certain configurations, the graphic structure 28 may lie on an arc or circular path.

[0148]

[0170] In one form, these controls may be "simulated" with a remote control application on a smartphone connected, for example, wirelessly or via the Internet, to a main network device 14 or to a "satellite" network device 14 forming the home network.

[0149]

[0171] Alternatively, these controls may be embodied in a TV remote control or game controller.

[0150]

[0172] Alternatively, these controls may be replicated on smaller versions of the network appliance 14 that are wirelessly connected to the main network appliance 14 or to the "satellite" network appliances 14 that form the home network.

[0151]

[0173] 6 and 7, these UI concepts enable streamlined control of the operation of network devices 14, including, inter alia, the selection of digital content 13 for viewing on the audiovisual display device 18. Importantly, the UI reflects the physical user experience, such as arcs of menus and images, concentric circles (or arcs) indicating menus or titles, a fusion of menus and images, and in one embodiment, a fusion of circles and arcs. In another embodiment, the menus and images may be displayed in a grid of tiles.

[0152] First concrete example of implementation

[0174] In a preferred form, the network device 14 includes at least the following functionality: -Connect to the internet via WiFi or an Ethernet cable -Connect to your TV via HDMI or WiFi interface -Connects to USB or HDMI for TV control -Connect to other devices, e.g. tablets, PCs via WiFi or Ethernet -Secure Peer Assisted Standard Network Client -Microsoft PlayReady Secure Client -Reliable execution environment - Movie, TV and game playback - "Remote" functions, such as seek, pause, rewind, fast forward, slow motion, via an app or via a device or small version of a device wirelessly connected to the "Home" device -Remote functionality via TV control, game controller, keyboard, trackpad or mouse -Stream, download and save all your content (mass storage options available) -Sideload games onto other gaming platforms, tablets and phones -HD and UHD ("4K") -Manage your library with third-party content -Share content securely. Content is DRM protected and provides a mechanism to purchase keys to unlock the content -Record metrics / statistics and send them to management systems -Monitor and manage the behavior and performance of your content -Monitor and manage network behavior and performance -Media Hub

[0153]

[0175] The overall topology of an example system may be as illustrated in Figures 6, 7 and 8, and have the following functional specifications:

[0154] Function Description

[0176] An embodiment of the network appliance 14 of the present invention comprises a device operating in accordance with the Secure Peer Assist Protocol 19, which is a portable device for downloading, storing, streaming, playing, and sharing high-quality movies, games, and television on a television or connected device. This embodiment combines Secure Peer Assist Standard 19 technology with a content origin store 11 and a syndicated retail content web store 41 to deliver the latest Hollywood and Indian movies, television, and games to televisions in True HD and UHD. An embodiment of the network appliance 14 addresses a key problem in today's OTT and IP TV distribution: the exponential growth of video traffic. In this example, the network appliance 14 provides greater flexibility to a new generation of content owners who can choose what they want to watch, when they want to watch it, and how and with whom they want to share it, in True HD and Ultra HD. function: -Connect to the internet via WiFi a, b, g, n, ac or an Ethernet cable to download and stream movies and TV from the GT TV store -Bluetooth option to connect to, for example, a TV or other devices -Connect to a TV via HDMIv2.0a and HDCP2.2 interface or later versions as the standards evolve - Connection via Full HD 1080p60, UHD (4K UHDTV 2160p 3840x2160) and HDCP secure HDMI connection or fiber to a high quality audio sound system that supports high quality audio, e.g. Dolby 5.1 or 7.1 - Supports a wide range of video encoding standards, including all H.264 codecs -H.265, HEVC, VP9 and Alliance for Open Media Codecs - 3X USB interfaces for connection to other devices, peripherals and TV controls -PSU for power supply -Connect to other devices, e.g. phones, tablets, PCs via WiFi or Ethernet - Remote control and first purchase in store -Streaming via Miracast and DRM -Secure peer-assisted network client in a secure environment - "Remote" functions, such as movie, TV and game playback, including seek, pause, rewind, fast forward and slow motion -Microsoft PlayReady Secure Client -Sideload games to other gaming platforms, tablets, and phones via Ethernet, WiFi, or USB (future release) -Stream, download and save all content (mass storage options available) -HD and UHD -Manage your library with third-party content -Share content securely -Record and send metrics / statistics to management systems Content behavior and performance Network behavior and performance -Media Hub - Universal Plug and Play (UPnP)

[0155] Model

[0177] All models are designed for a single enclosure to minimize production costs. This is a device of high aesthetic form and functionality with a simple and innovative human interface. The models are designed to appeal to the mainstream market as well as the super early adopter market. The models are very easy to use.

[0156]

[0178] Base Model: This is the base model with a minimum 2TB disk and 128GB SSD storage. It is a fully functional peer in a secure peer-assisted network, allowing high-quality downloads and streaming of movies and TV from Store 41. It is controlled via the unit, via a phone or tablet app, or via a TV remote or keyboard, trackpad, or mouse.

[0157]

[0179] Base model with disk library: This is the base unit with a minimum 2TB 2.5-inch disk drive for storing movies. It can store 200-400 HD movies or 100 UHD movies, depending on the encoding size.

[0158]

[0180] SSD model with SSD library: This is the base unit with a 250GB to 2TB SSD hard drive. It can store up to 100 UHD movies, depending on the encoding size.

[0159]

[0181] Media Hub and Streaming: This enables secure streaming of digital content to CE devices such as phones and tablets, as well as streaming of user content to televisions.

[0160] Network device control app

[0182] The network appliance 14 may be controlled by an app on a phone or tablet. This may initially be an Android or iOS app for iPhones and tablets. Other applications will be implemented in the future. It may provide complete remote control of all display functions as well as the ability to make purchases directly through a store accessible to the network appliance.

[0161]

[0183] It may optionally control the TV remotely via USB or Bluetooth if equipped, or via network appliance 14.

[0162] power supply

[0184] In the preferred form, the system should be as low power as possible. The system may be powered from an AC power pack. The system may optionally be battery powered.

[0163] operating system

[0185] The system may run a secure real-time version of the Linux operating system or the Microsoft Windows operating system.

[0164] architecture

[0186] In the system of Example 1, the system architecture may be ARM Cortex A9 or later including ARM TrustZone, or Intel Core Architecture 6th generation or later including Secure Guard Extensions (SGX), Memory Protection Extensions (MPX), Secure Enclaves and Hardware DRM.

[0165] Security

[0187] In the system of Example 1, all media files are DRM encrypted. The preferred DRM is Microsoft PlayReady for movies and Ubisoft DRM or Tages Solid Shield for games, although other studio-approved DRMs may be used, including Adobe Access and Google Widevine. The system may provide a robust, long-term solution where trusted applications are added in the field over the life of the device. The system may comply with the Trusted Execution Environment specification. The system may support a trusted launch mode and trusted control of all I / O ports.

[0166]

[0188] The system may support Intel's Secure Guard Extensions (SGX), Memory Protection Extensions (MPX), Secure Enclaves, and Hardware DRM.

[0167]

[0189] The system may support secure authentication and sealing.

[0168]

[0190] The system may support the ARM Advanced System Architecture and Base Architecture Platform for Digital Rights Management (DRM) and integrates a TrustZone Address Space Controller (TZASC) that protects areas of RAM used to hold valuable content.

[0169]

[0191] The architecture may support the integration of media accelerators such as GPUs, video engines and display controllers, all of which require knowledge of the security state of the processor.

[0170]

[0192] The system may provide tamper protection and a real-time clock.

[0171]

[0193] The system may support secure hardware cryptographic acceleration to optimize DRM decryption speed. The system may support high-assurance activation and recognition of digitally signed software.

[0172]

[0194] The system may support secure JTAG - JTAG is restricted from use (at a no-debug level) unless a secret key challenge / response protocol is successfully executed.

[0173] DRM

[0195] The preferred embodiment of the system supports Digital Rights Management (DRM). For movies and TV, Microsoft PlayReady is preferred, and for games, initially Ubisoft DRM or Tages Solid Shield is preferred. Other studio-approved DRMs, such as Adobe Access and Google Widevine, are alternatives. Hardware and O / S -Current hardware and O / S specifications -I / O ports / antennas -AC power adapter -3 x USB2.0 -1 x 1000Mb Ethernet -HDMI2.0a connector -WiFi a, b, g, n, ac -HiFi sound optical or HDMI -Mass storage -2.5-inch disk drive, 2TB minimum -SSD128G-1TB minimum

[0174]

[0196] Referring to FIG. 14, a conceptual flow diagram for syndication of digital content 13 is shown.

[0175]

[0197] In summary, a system of Example 1 is described, which is preferably implemented via a network appliance 14 of the type described with reference to FIG.

[0176]

[0198] Preferred forms of criteria for receiving data packets at a network device operate according to one or more of the following, either alone or in combination: a. Most needed packets b. Fastest download speed c.Minimum latency d. A network address in which the next digital bit or group of bits can be most easily and efficiently obtained to maintain real-time or near real-time delivery of digital content.

[0177]

[0199] Preferably, digital content, and particularly certain items of digital content, are "wrapped" with DRM, delivered to network devices, and decrypted on the network devices using the Microsoft PlayReady infrastructure.

[0178]

[0200] Referring to FIG. 11, the system 10 is conceptualized from a user's perspective.

[0179]

[0201] Broadly speaking, in this example there is a "Super PoP" that combines an aggregator database 12, an origin store 11 and a data packet address database / network management server 40, which in conjunction with distributed network equipment 14, and preferably using the Internet as the primary communication channel, coordinates the efficient and timely delivery of data packets 24 (forming identified items 17 of digital content 13), thereby enabling secure and timely delivery of a wide range of digital content to users 42.

[0180]

[0202] The system enhances the experience for all stakeholders by providing digital data creators and rights holders with confidence in the security of their digital data, while offering a wide range of digital content for user 42 selection, all delivered in a controlled and timely manner so that both virtually real-time streaming and data downloads are available over a wide range of internet connections.

[0181] Further Preferred Embodiments

[0203] 12, 13 and 14, there is shown a basic platform and functional implementation of a further embodiment of the present invention that can be implemented using an Intel branded chipset and Microsoft Windows branded software modules.

[0182]

[0204] It will be appreciated that at least some embodiments of the present invention advantageously operate in a highly secure manner, so that potentially valuable software, such as ultra-high definition (UHD) movies, can be processed without risk of unauthorized access or use.

[0183]

[0205] Typical UHD movies operate according to MPEG4 standards such as H.264 (so-called HD resolution, which typically operates at 1080 pixels or lines below the screen) and H.265 (so-called 4K or UHD resolution, which typically operates at 2160 lines or pixels below the screen). Typical file sizes for such movies are around 15-20 GB. In a currently more preferred embodiment, the "Secure Peer Assist" configuration described in the previous embodiment is enabled on a Windows / Intel platform.

[0184]

[0206] 12, there is shown a circuit board 111 that includes at least a trusted platform module (TPM) 112 in communication with a processor 113 and memory 114. Alternatively, the TPM may be embodied in the processor 113 or an associated system module.

[0185]

[0207] The trusted platform module 112 includes a unique identifier 115 , a certificate 116 for encryption and decryption, and secure boot code 117 .

[0186]

[0208] The Trusted Platform Module 112 implements the Trusted Computing Group architecture on hardware that, in this example, is part of the TXT platform available from Intel's Secure Guard Extensions (SGX), Memory Protection Extensions (MPX), Secure Enclaves, and Hardware DRM.

[0187]

[0209] In a preferred configuration where the TPM is embedded in the processor or an associated module, the processor supports Intel's Secure Guard Extensions (SGX), Memory Protection Extensions (MPX), Secure Enclaves, and Hardware DRM.

[0188]

[0210] In its preferred form, DRM is implemented using the Microsoft PlayReady environment. In this configuration, UHD 4K content will play if and only if:

[0189]

[0211] If a hardware DRM environment is detected

[0190]

[0212] The environment is within a trusted execution environment, and

[0191]

[0213] All video outputs are implemented using the preferred output protocol, a particular preferred example being HDCP2.2.

[0192]

[0214] During operation, the trusted platform module 112 allows the processor 113 to enter a trusted execution state.

[0193]

[0215] A preferred operating system loaded into memory 114 for execution by processor 113 is the Microsoft Windows 10 operating system or later version.

[0194]

[0216] 13, processor 113 and memory 114 may optionally execute a virtual machine 118 within an Intel architecture environment. Virtual machine 118 allows direct hardware access by an operating system, such as the Windows 10 operating system, while running within a highly secure environment. A movie file 119 downloaded to memory 114 using the secure peer-assisted configuration of the previous embodiment may be processed, and the video stream is decrypted via a hardware DRM and HDCP level shifter protocol converter (LSPCON) chip 120 for secure output, preferably via an HDMI, DisplayPort, or Thunderbolt connection, to an ultra-high resolution display device 121.

[0195]

[0217] Alternatively, the video stream may be securely routed to secure GPU 120A for secure output via HDMI.

[0196]

[0218] Referring to Figure 14, there is shown a schematic representation of the flow of data on platform 111. Preferably in the form of secure peer-assisted platform 122, movie files 119, potentially assembled from many sources, are processed by components including an optional virtual machine 118 running within a Windows 10 environment utilizing hardware DRM to provide a highly secure output stream 119A, which is processed by converter chip 120 (preferably an HDCP2.2 LSPCON chip) to output a secure video stream 119C displayable on an ultra-high resolution display device 121.

[0197]

[0219] The trusted execution environment and streams 119A are protected via data 119B provided from an independent security support and attestation server 123, as illustrated in FIG.

[0198]

[0220] The end result is an output stream 119C to an ultra-high resolution display device 121 that is decoded in real time while maintaining a high level of security, thereby enabling ultra-high resolution video files, such as UHD 4K resolution movie files, to be displayed in substantially real time according to Movielabs and Motion Picture Association of America specifications and high value content specifications of individual studios and content owners.

[0199]

[0221] FIG. 15 is a screenshot of a menu screen output on screen 121, which allows the user to select a movie to watch on display 121.

[0200]

[0222] FIG. 16 is a screenshot of a menu selection screen where the user can select a movie to watch on display 121 using a scrolling configuration.

[0201]

[0223] FIG. 17 is a screenshot of the selector screen layout.

[0202]

[0224] In certain embodiments, a user may utilize associative technology that clusters items for selection according to predetermined criteria. An example of such a system is described in U.S. Patent Application Publication No. 2014 / 0330841, the description, claims, and drawings of which are incorporated herein by cross-reference. In certain embodiments, a correlation algorithm is applied between items in a finite set of items, each item having a visual indicia of association and at least a set of attributes common to all other items in the finite set of items to facilitate discovery of the item within the finite set.

[0203]

[0225] In certain embodiments, a scoring system is used to quantify the degree of correlation.

[0204]

[0226] In a further preferred embodiment, Secure Peer Assist may be "inserted" or integrated into the adaptive bitrate protocol to take advantage of the wide range of existing assets and resources that use adaptive bitrate. This may be through direct integration or through an application programming interface (API). Secure Peer Assist handles network communications and interfaces to adaptive bitrate resources such as media servers, video encoders, segmenters / packagers, digital rights management systems, key management systems, content delivery networks, video players, browsers, and client applications. Secure Peer Assist manages the timely delivery of video and other content packets. To the adaptive bitrate protocol, it appears as an optimal single fixed-rate stream. In effect, this converts adaptive bitrate into progressive download or optimal fixed-rate streaming depending on the available user bandwidth.

[0205]

[0227] In a further preferred embodiment, Secure Peer Assist is integrated using Dynamic Adaptive Streaming over HTTP (DASH), also known as MPEG-DASH, and Common Encryption and Encrypted Media Extensions (EME). The proposed name for this arrangement is DSPASH (Dynamic Secure Peer Assist over HTTP). This preferred embodiment is integrated into HTML5 browsers that support media source extensions. This provides a standardized implementation and allows for the most efficient implementation across a multitude of consumer devices.

[0206]

[0228] A further preferred embodiment uses the Microsoft PlayReady DRM and the Microsoft Edge HTML5 browser on the above-described preferred embodiment of an Intel processor hardware platform that implements PlayReady in hardware under a tightly integrated Microsoft Windows 10 (or later) operating system.

[0207] Industrial Applicability

[0229] The network appliance may be implemented as a standalone hardware unit, or as multiple connected units programmed with the secure peer assist standard described above. Alternatively, the secure peer assist standard may be programmed into other devices such as smartphones, game controllers, smart TVs, etc.

[0208]

[0230] Server-based devices can be used to implement the aggregator 12 and origin store 11.

[0209] Introduction of SPAN-AI and UCDN embodiments of the media distribution management system

[0231] Referring to FIG. 1A, a block diagram of a SPAN-AI embodiment of the media delivery management system 10 shown in FIG. 2A is shown. The SPAN-AI embodiment includes subsystems that form a broadly applicable, universally operable, highly scalable, and efficient system for optimizing, managing, and operating a unified content delivery network (UCDN), as shown in FIG. 1. The unified content delivery network incorporates AI-driven secure peer-assisted networking (SPAN-AI), a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment. SPAN-AI recognizes the limitations of existing technologies that are only suitable for specific applications on a non-global scale, and uses an AI-driven hybrid routing approach to improve and adaptively combine the best features of existing solutions under a unified, secure, content-addressable architecture. We call this the unified content delivery network, or UCDN.

[0210]

[0232] UCDN creates a global network of interoperable peer networks, thereby eliminating the problems associated with the previous "network of networks" approach. UCDN does so through open standards, interfaces, protocols, and methods that allow any network to interoperate with any other network. These include, but are not limited to, AI and routing standards, interfaces, protocols, and methods.

[0211] Background technology

[0233] The first embodiment, referring to Figure 2A, teaches a hybrid ecosystem for peer-to-peer streaming and downloading, combining a semi-centralized (cloud) media distribution management server and super PoPs (Points of Presence) with distributed self-organizing intelligent edge nodes in a mesh network. The nodes form a content-based distributed storage network, utilizing centralized and distributed network knowledge to optimally deliver encrypted media content over the Internet and provide comprehensive network-wide Quality of Service (QoS) monitoring, control, and optimization. The system, previously referred to as Secure Peer Assist (SPA), is now known as Secure Peer Assisted Networking, or SPAN; incorporating AI, it is known as SPAN-AI, and incorporating a hybrid adaptive routing design, it is known as SPAN-AI-HARD.

[0212]

[0234] The first embodiment with reference to Figure 2A further teaches that routing information may be distributed and / or centralized and may be in the form of a hash table or other efficient database mechanism. This detailed knowledge combined with control of network devices 14 and routing is a form of Software Defined Networking (SDN).

[0213]

[0235] In particular, it is further taught that "network knowledge" includes address information for all data packets forming the digital content 13, and more particularly, for all data packets that form part of the identified item 17 in any event. This data packet address information may be stored in a database 40 as shown in Figure 3, where each individual address, e.g., address AA of each data packet 24, is linked to a location, in this example loci.

[0214]

[0236] Database 40 may be stored in or form part of origin store 11, or may be a separate server. In other examples, database 40 may be stored at least partially in memory 21 of individual network devices 14 to provide a distributed storage arrangement. It will be appreciated that over time, a significant number of widely distributed sources will become available from which identified items 17 may be downloaded (in whole or in part).

[0215]

[0237] Secure Peer Assist standards and application programs based on them recognize and report network traffic at the SCTP, TCP / IP, UDP, and video packet levels. Each network device 14 forms an intelligent node in a mesh network, which may also be described as grid computing or distributed cloud computing. It combines distributed and centralized routing information and intelligence down to the video packet level. This enables optimal management of networks with software-defined network-like capabilities.

[0216]

[0238] The Secure Peer Assist standard enables the creation of an entire ecosystem for managing video and game distribution over the Internet. Each network device 14 monitors metrics and statistics at the network and video packet level, reporting traffic and video status in real time. Combining the video asset management distribution platform with a Super PoP CDN provides comprehensive quality of service (QoS) monitoring and control across the network. The Secure Peer Assist standard provides a highly efficient method of video distribution over the Internet that minimizes network load and maximizes network and customer viewing performance. The Secure Peer Assist standard may also be implemented in consumer electronics (CE) apps.

[0217]

[0239] The Secure Peer Assist Standard (SPAC)19 extends network reach beyond the edge to the customer's home.

[0218]

[0240] The first embodiment, referring to FIG. 2A, describes a system for managing and optimizing telecommunications networks. In a preferred embodiment, the system is for managing and optimizing the Internet, telecommunications carriage networks, and content delivery networks (CDNs). The SPAN-AI and UCDN embodiments build on the SPAN system—its components (in addition to the SPAN components) are shown in FIG. 2A. Finally, the additional functionality comprising the SPAN-AI and UCDN embodiments teaches unified content delivery network (UCDN) methods and extends the SPAN system's distributed storage network methods, thereby integrating and optimizing all Internet, telecommunications carriage networks, and content delivery networks (CDNs) into a single, unified, optimized network. In a preferred embodiment, this is done through state-of-the-art methods of Secure Peer-Assisted Networking (SPAN) machine learning and artificial intelligence hybrid adaptive network design, or SPAN-AI-HARD.

[0219]

[0241] In conjunction with the SPAN system, there are other initiatives and projects that define the current state of the art. These include the Named Data Network Project, i、ii ;Information-centric networking iii、iv and IPFS v These other initiatives are not complete solutions. They effectively form subsystems of the general, scalable solution that is the UCDN embodiment. Each has its own limitations, particularly in terms of scope and growth. UCDN, incorporating SPAN-AI and AI HARD, overcomes these limitations.

[0220] Overview of SPAN-AI and UCDN embodiments

[0242] The present invention combines and extends these subsystems to form a broadly applicable, universally operable, highly scalable, and efficient system for optimizing, managing, and operating a Unified Content Delivery Network (UCDN) incorporating AI-driven Secure Peer-Assisted Networking (SPAN-AI), a hybrid adaptive networking technology that provides global, scalable, and secure distributed content storage, computation, and delivery for any application and network environment. SPAN-AI recognizes the limitations of existing technologies that are only suitable for specific applications at a non-global scale, and uses an AI-driven approach to improve and adaptively combine the best capabilities of existing solutions under a unified, secure, content-addressable architecture. We call this the Unified Content Delivery Network, or UCDN.

[0221]

[0243] UCDN creates a global network of interoperable peer networks, thereby eliminating the problems previously associated with the "network of networks" approach. UCDN does so through open standards, interfaces, protocols, and methods that allow any network to interoperate with any other network. These include, but are not limited to, AI and routing standards, interfaces, protocols, and methods. UCDN creates a global network of interoperable peer networks, thereby eliminating the problems previously associated with the "network of networks" approach. UCDN does so through open standards, interfaces, protocols, and methods that allow any network to interoperate with any other network. These include, but are not limited to, AI and routing standards, interfaces, protocols, and methods.

[0222]

[0244] A UCDN is formed from one or more interoperable peer networks.

[0223]

[0245] The UCDN network may comprise a peer network in the form of a SPAN_AI network.

[0224]

[0246] These are made interoperable through the use of open standards, interfaces, protocols or methods. In a preferred embodiment, this may be a network of one or more SPAN-AI networks that interoperate via AI, routing or other interfaces (see Figure 1B).

[0225]

[0247] Any network may be transformed into a SPAN-AI network by "injecting" a SPAN-AI agent into the network (distributing containerized microservices or applications) and incorporating SPAN-AI Intelligent Hybrid Adaptive Routing (AI-HARD) and SPAN-AI Global Optimization AI into the network.

[0226]

[0248] Alternatively, any network may be interconnected to form a UCDN by connecting to a compatible open standard interface, protocol, or method (API) of the SPAN-AI network, preserving compatibility and communication with "legacy" networks (see FIG. 1C). Preferably, these networks are converted to the SPAN-AI network.

[0227]

[0249] A minimal embodiment of the SPAN-AI network comprises a network of self-organizing peers and agents incorporating AI-HARD intelligent hybrid adaptive routing with globally optimized AI. Other embodiments may include any additional capabilities or functionality. The core SPAN-AI system:

[0228] routing

[0250] SPAN-AI's routing protocol, AI-HARD (Hybrid Adaptive Routing Design), consists of two subsystems: a storage-centric routing subsystem and a delivery-centric routing subsystem; it combines the benefits of Name Resolution-Based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name Based Routing (NBR) for fast and reliable content delivery. AI-HARD also combines IP routing to provide backward compatibility.

[0229]

[0251] The AI-HARD intelligent agent within SPAN-AI uses predictive knowledge of network conditions and application requirements to adaptively select the most efficient routing policy from the subsystems.

[0230]

[0252] The publication of the AI-HARD protocol will enable its incorporation into existing as well as new routers, ensuring routing compatibility across all networks.

[0231] Naming and Discovery

[0253] SPAN-AI's Unified Naming and Discovery System (UND) i) maps human-readable, mutable names (e.g., domain names, content names) to immutable, self-certifying CIDs, and ii) allows routing of CIDs through both the NRR and NBR subsystems by adding a name prefix to the beginning of each content identifier. UND also combines IP-DNS to ensure backward compatibility.

[0232]

[0254] In another preferred embodiment, SPAN-AI's Unified Naming and Discovery System (UND) i) maps human-readable, mutable names (e.g., domain names, content names) to immutable, self-certifying CIDs, and ii) allows routing of CIDs through both the NRR and NBR subsystems by combining name prefixes and content identifiers in a way that optimizes routing and / or storage. UND also combines IP DNS to ensure backward compatibility.

[0233]

[0255] UND discovery includes both SPAN-AI's smart discovery service AmI Rendezvous and IP name discovery, or DNS.

[0234]

[0256] The publication of UND naming standards and rules will enable interoperability between existing IP networks and new SPAN-AI networks. The rules include IP naming, e.g., DNS.

[0235] Publish-Subscribe (pub-sub)

[0257] SPAN-AI uses an AI-driven pub-subsystem to provide an asynchronous multi-party distribution service, including communication between AI agents, naming services, and discovery services, including interoperation with IP discovery services.

[0236] Global Optimization AI

[0258] SPAN-AI provides optimization at a global level by "rolling up" data from hierarchical AI agents with varying degrees of awareness and optimization capabilities at the peer, edge, core, and other network levels. This provides a global view and enables global optimization. The data and protocols describe the SPAN-AI system using a formal logical ontology and semantics.

[0237]

[0259] Publishing open interfaces allows AIs in other networks to communicate with SPAN-AI and between them. These interfaces utilize SPAN-AI semantics and ontologies and may utilize open software-defined networking (SDN) standards such as OpenFlow.

[0238] Self-organizing swarm intelligence

[0260] SPAN-AI orchestrates the adaptive operation of routing and pub / subsystems through a family of pluggable hierarchical (local / edge / global / other) AI agents that provide monitoring, prediction, optimization and control services at various levels of awareness and optimization capabilities at peer, edge, core and other network levels.

[0239]

[0261] These agents form self-organizing swarms using distributed control and machine learning models to optimize behavior at local and edge levels, providing adaptability and recovery to dynamic events such as mass churn.

[0240]

[0262] Swarm intelligence allows other swarms to join and become part of the network.

[0241]

[0263] SPAN-AI, AI-driven Secure Peer-Assisted Networking, is a hybrid adaptive networking technology aimed at providing global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment.

[0242]

[0264] SPAN-AI recognizes the limitations of existing technologies that are only suited to specific applications at a non-global scale, and leverages an AI-driven approach and hybrid adaptive routing to improve and adaptively combine the best capabilities of existing solutions under a unified, secure, content-addressable architecture, which we call the Unified Content Delivery Network, or UCDN.

[0243] Glossary:

[0265] Peer: Any hardware or software device that has a similar or equivalent general or specific purpose in whole or in part.

[0244]

[0266] P2P: Peer-to-peer.

[0245]

[0267] Agent: A software application with varying degrees of awareness, communication, optimization, learning, reporting, self-organization, or other capabilities that is distributed to any network device (computer, appliance, router, switch, server, etc.) and / or runs on network equipment; virtual network services or applications; this can be an AI agent; an application running on a virtual peer, i.e., an operating system running in a virtual environment; a network service; etc.

[0246]

[0268] I / F: Interface. A method of interconnecting software or hardware applications with each other or with people for the purpose of communication. In a preferred embodiment, this method is open and standardized, in which case the interface may be known as an Application Programming Interface, or API.

[0247]

[0269] Peer Network: Any network that has, in whole or in part, a similar or equivalent general or specific purpose.

[0248]

[0270] IP: Internet Protocol; the original and current "thin-waist" routing protocol of the Internet

[0249]

[0271] TCP: Transmission Control Protocol

[0250]

[0272] SPAN: Secure Peer-Assisted Networking

[0251]

[0273] AI: Artificial intelligence

[0252]

[0274] ML: Machine Learning

[0253]

[0275] AmI: Ambient Intelligent

[0254]

[0276] HARD: Hybrid Adaptive Routing Design

[0255]

[0277] SAMSim: A self-aware mesh simulator

[0256]

[0278] CID: Content Identifier

[0257]

[0279] IPFS: Interplanetary File System

[0258]

[0280] IPLD: Interplanetary Link Data

[0259]

[0281] IPNS: Interplanetary Name System

[0260]

[0282] DNS: Domain Name System

[0261]

[0283] DNSLink: A protocol that uses DNS text records to link domain names to IPFS addresses or CIDs.

[0262]

[0284] NDNS: Domain Name System for Named Data Networking

[0263]

[0285] mDNS: Multicast DNS

[0264]

[0286] pub / sub: Publish / Subscribe

[0265]

[0287] libp2p: A modular, location-independent network stack. Part of IPFS.

[0266]

[0288] NRR: Name Resolution Based Routing

[0267]

[0289] NBR: Name-Based Routing

[0268]

[0290] NDN: Named Data Networking

[0269]

[0291] NBN Either Australia's Name-Based Networking or National Broadband Network

[0270]

[0292] DHT: Distributed Hash Table

[0271]

[0293] DRM: Digital Rights Management

[0272]

[0294] VoD: Video on Demand

[0273]

[0295] ISP: Internet Service Provider

[0274]

[0296] CDN: Content Delivery Network

[0275]

[0297] PoP: Point of Presence

[0276]

[0298] FIL: Abbreviation for Filecoin crypto token exchange

[0277]

[0299] testlab and testground: IPFS testing frameworks

[0278]

[0300] PoC: Proof of Concept

[0279]

[0301] MVP: Minimum Viable Product

[0280]

[0302] NRT: Near real-time or non-real-time

[0281]

[0303] ISO: International Organization for Standardization

[0282]

[0304] QoS: Quality of Service

[0283]

[0305] Telecommunications

[0284]

[0306] telco: telecommunications company

[0285]

[0307] Node: A vertex in a graph network model; a connecting point of graph edges;

[0286]

[0308] Edge: A network edge (1-2 hops away from the end-user device); or a connection between nodes in a graph;

[0287]

[0309] Graph: A mathematical model used to represent communication networks, data organizations, computing devices, computation or communication flows, etc.

[0288]

[0310] UND: Integrated Naming and Discovery / Directory System / Service

[0289] Introduction

[0311] SPAN-AI, AI-driven Secure Peer-Assisted Networking, is a hybrid adaptive networking technology that provides global, scalable, secure, distributed content storage, computation, and delivery for any application and network environment.

[0290]

[0312] SPAN-AI recognizes the limitations of existing technologies that are only suitable for specific applications at a non-global scale. SPAN-AI uses an AI-driven approach to improve and adaptively combine the best capabilities of existing solutions under a unified, secure, content-addressable architecture, which we call the Unified Content Delivery Network, or UCDN.

[0291]

[0313] SPAN-AI is based on five key systems: unified naming; unified discovery; hybrid routing; scalable pub-sub; and embedded security; all securely integrated and co-optimized through a hierarchical, pluggable AI framework with associated simulation, training and development pipelines that incorporate AI agents with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.

[0292] SPAN-AI Architecture

[0314] SPAN-AI uses a unified naming discovery system that i) maps human-readable, changeable names (e.g., domain names, content names) to immutable, self-certifying CIDs, and ii) enables routing of CIDs through both the Name Resolution Routing Subsystem and the Name-Based Routing Subsystem, either by iii) adding a name prefix to the beginning of each Content Identifier (CID) or by iv) combining names with CIDs in a way that optimizes routing and / or storage. 1. Integrated Naming System a.SPAN-AI is content addressable b. Content items or blocks are identified via immutable, self-certifying content identifiers (CIDs), like IPFS c. A global distributed naming directory service is used to map human-readable, mutable names / links to immutable CIDs i. Initially, IPNS and / or DNS links are used ii. Extensions include the use of NDNS d. The CID is then resolved (CID-provider mapping and provider-requester path formation) via a hybrid adaptive routing system (system 3). i. Name resolution based routing, i.e., querying a (multi-level) DHT ii. Name-based routing, i.e., prefix (e.g., SPAN / <cid>Hop-by-hop forwarding of packets of interest with e. Extensions include hierarchical names and name-based routing for name-to-CID mapping f. SPAN-AI intelligence determines where to host the decentralized naming service (see SPAN-AI Intelligence section) g. The SPAN-AI pub / subsystem is used for scalable, fast distribution of naming updates (see Scalable Pub / Subsystem).

[0293]

[0315] The federated naming system may use JSON updates with conflict-free replicated data types (CRDTs) using cryptographic key-value pairs, which may be structured as DHTs, Merkle trees, simple blockchains, or other efficient distributed data structures.

[0294]

[0316] SPAN-AI employs an AI-driven integrated discovery system, the key component of which is Ambient Intelligent Rendezvous (AmI Rendezvous), which provides smart discovery, configuration and self-organization services. 2. Integrated Discovery System (AmI Rendezvous) a. Providing smart discovery, configuration and self-healing services Bootstrapping i.Nodes, discovering peers, and maintaining the DHT and pub / sub overlay b. Combining peer-level self-healing intelligence with edge-level smart discovery (see AmI rendezvous operations in the SPAN-AI Intelligence section) c. SPAN-AI intelligence determines where to host distributed AmI rendezvous services (see SPAN-AI Intelligence section) i.AmI rendezvous would ideally be hosted alongside edge-level naming and intelligence services ii. Peers register at initialization time after performing mDNS and DHT discovery. iii. Data partitioning and service placement may be guided by naming.

[0295]

[0317] SPAN-AI addresses large-scale routing via an AI-driven hybrid adaptive routing design (AI-HARD) consisting of two subsystems that aim to combine the benefits of Name Resolution-Based Routing (NRR) for scalable, available, and accessible distributed storage with the advantages of Name-Based Routing (NBR) for fast and reliable content delivery. AI-HARD includes the storage and distribution markets. 3. AI-driven hybrid adaptive routing design (AI-HARD) 3.1 Storage-centric Routing Subsystem a. The primary goal is persistent data availability (all content must be reachable) and relatively fast content access (sub-second). b. Name Resolution Based Routing (NRR) via a new parameterized multi-level DHT incorporating: i. Learned information about user requirements (e.g., content popularity, delivery deadlines) and network / topology structure (e.g., hop distance, latency, load) is used to create multiple, limited-size, fast lookup layers. ii. Topology Layer 1) Each DHT contains only topologically adjacent nodes. 2) Local, regional and national DHTs iii. Topic layer 1) Each DHT contains only content related to a specific topic. iv.Heterogeneous layer 1) Each DHT contains nodes that are adjacent and share similar interests. c. Intelligent content placement with adaptive replication levels i. Content is replicated according to learned interest / popularity and network connectivity / stability (more replication under high churn / volatility) ii. Replication levels are optimized to guarantee the lookup+delivery latency requirements of "storage-centric" applications. Additional in-network caching is provided for delivery-centric applications (see subsystem 3.2). d. Multi-level DHT structure and related parameters (layers, participant nodes, bucket size, concurrency factor) and content replication are dynamically adjusted based on AI-driven optimization and distributed control algorithms (see SPAN-AI Intelligence's AI-HARD Operation). e. Multi-level DHT and smart content replication solutions enable maximizing the number of locally resolved queries to provide efficient, scalable and persistent content access. f. Content-level (rather than chunk-level) transfer state for further scalability g. Integrates with name resolution-based subsystems through a common unified name directory service (System 1) 3.2 Delivery-centric Routing Subsystem a. The primary goal is fast content delivery (<100ms) b. Name-Based Routing (NBR) for fast lookup and delivery (e.g., NDN) i. Data plane recognition symmetric data packet forwarding ii. In-Network Caching iii. Native Multicast and Mobility Support iv. In-network load balancing c. Integrated with the NRR subsystem via a common unified name directory service (System 1) d. Used only for applications with real-time requirements (e.g., video streaming) i. Reserved for applications that can benefit from faster and more efficient application-level (as opposed to network-level) aware content delivery ii. Dramatically reduce forwarding state (the NRR subsystem handles apps with non-real-time requirements) iii. Maintaining chunk-level forwarding state allows for leveraging path diversity and further accelerating content delivery. 3.3 Market Enablement a. Storage-centric i. Issuers may choose and pay for appropriate storage metrics (reliability, replication, decentralization, persistence, etc.) in a market such as Filecoin. ii. SPAN-AI will support and integrate multiple storage markets and technology platforms into a unified content storage delivery network, which may include storage markets and platforms such as blockchain. b. Delivery-centric i. Issuers can choose and pay for appropriate delivery metrics (resolution, bitrate, latency, etc.) in a marketplace similar to Filecoin. ii. Delivery providers (telcos, ISPs, CDNs, etc.) may bid for delivery in the same market or rely on SPAN-AI and AI-HARD to select the most efficient path, thereby incentivizing efficiency. iii. Consumers may choose which merchant(s) they wish to use, for example, if they enter into a contract with any of them. Consumers may freely choose whether and with whom to enter into a contract, or may contribute to and be rewarded by one or more common pools. iv. Delivery preferences may be expressed by the consumer in the name request, e.g.: SPAN: / / Warner Bros / Batman / Director's Cut / 4K / <My Address> / Telstra / Akamai / (actual names and order may vary depending on naming and routing considerations) v. Publishers may choose a default distribution partner. In the event of a conflict, SPAN-AI and AI-HARD will select the most efficient path, again incentivizing efficiency. vi. When consumers or publishers do not specify delivery preferences, SPAN-AI and AI-HARD select the most efficient path. vii. If one or more merchants selected by the consumer or issuer is not the most efficient for any given routing case, SPAN-AI and AI-HARD will select the most efficient path and inform all stakeholders of the decision, allowing them to optimize efficiency. viii. Payments for delivery will be calculated and executed by one or more settlement systems informed by the SPAN-AI and AI-HARD routing systems in a manner similar to how telephony call settlement is executed today. ix. Anyone may contribute resources and be rewarded for their contributions, providing a free market for telecom services. SPAN-AI monitors and maintains the security and health of the network. Underperforming resources are removed. SPAN-AI is designed to work with both commercial and telco-grade resources and meet QoS levels. QoS metrics and costs determine resources used and vice versa.

[0296]

[0318] SPAN-AI uses an AI-driven pub-subsystem for control plane distribution: directory updates (names, discovery, configuration) and intelligence updates (optimization / control operations); and data plane distribution: asynchronous multi-party distribution services supporting collaborative applications, live streaming, etc. 4. Scalable pubs / subsystems a. Fast, scalable, asynchronous multi-party distribution services b. Pub / Sub for Control i. Name Directory Update ii. Discovery updates (new peers, new services) iii. Configuration updates (new roles, new memberships) iv. Intelligence updates (optimization / control commands, e.g., resource allocation, storage and routing decisions) c. Pub / sub for data i. Collaborative Media Apps ii. Live Streaming d. The pub / subsystem will use an evolution of existing pub-sub algorithms such as Gossipsub, PlumTree, and HyParView, using the AmI rendezvous service for operational improvements. i.AmI rendezvous smart discovery and self-healing improves scalability and churn resilience with little impact on the routing scheme other than adjusting the degree of overlay, fanout and probability weights. ii. Built-in plugins for self-healing and smart discovery enhance peer discovery, activation and lifecycle maintenance of overlays. 1) The pub / subsystem is embedded with pluggable indicators, actuators and triage for smart discovery. 2) A periodic heartbeat distributes health indicators for the AmI rendezvous mesh.

[0297]

[0319] SPAN-AI incorporates integrated security at all levels. It uses machine learning and machine perception to detect and manage security threats. Content can be encrypted using commercial DRM systems such as PlayReady before being published to the system. Publishers can cryptographically sign data packets. Naming is rooted in self-sovereign identity, which can be defined as a lifelong, portable digital identity that does not rely on any centralized authority. It uses decentralized identifiers that provide persistence, global resolvability, cryptographic verifiability, and decentralization. Names can be self-certified. Preferred embodiments are based on a hardware root of trust and secure boot. Further preferred embodiments may utilize web of trust methods. Quantum cryptography, i.e., encryption based on quantum state random number generators, may be used. 5. Comprehensive Security Architecture and Preferred Implementation a. Machine learning and machine perception to detect and manage security threats b. Cryptographic packet signing c. Encryption and DRM d. Hardware Root of Trust e. Secure Boot f. Decentralized Identifiers, which may utilize Web of Trust methods. g. Sovereign Identity h. Naming rooted in sovereign identity i. Self-certified name using CID with prefix. j.Quantum cryptography

[0298] SPAN-AI

[0320] SPAN-AI coordinates the adaptive operation of routing and pub / subsystems through a family of pluggable hierarchical (local / edge / global / other) AI agents that provide monitoring, prediction, optimization and control services with varying degrees of awareness and optimization capabilities at peer, edge, core and other network levels.

[0299]

[0321] SPAN-AI uses a simulation, training and development pipeline that enables cloud-level replication of the runtime environment, simulation, testing and training of AI models, which can be plugged into peer / edge / core / other network nodes for real-time optimization and control.

[0300]

[0322] SPAN-AI provides a marketplace of pluggable AI agents to enable open and flexible innovation in the optimization and control of universal networks, which may be based on crypto tokens such as Filecoin or Blast. 1.Hierarchical AI a. Hybrid local / global optimization and control i. Combine fast, local, reactive self-organization with slower, global / hierarchical, proactive guidance / supervision and backup support. b. Hierarchical Intelligence i. Local intelligence at the peer level 1) Local monitoring and rapid response for basic survivable operations and self-organization 2) Fast and simple rules (e.g., filters, thresholds) 3) Nodes with limited functionality ii. Ambient / Swarm Intelligence at the Edge 1) Higher level services supporting discovery, bootstrapping, configuration, resource allocation, role assignment, storage decisions, routing hints, pub / sub membership, and naming 2) Ideal location for ML models 3) Nodes with higher functionality, reliability and stability iii. Core-level global intelligence 1) Optimization based on a global view 2) Training ML models that are pushed down to the edge 3) AI simulation 4) Nodes with the best capabilities (e.g., stable peers, cloud nodes, ISP cores, CDN PoPs) c. Application and Network Awareness i. Global predictive knowledge of user consumption / production patterns, application requirements, network conditions (including overlay mesh health) and available resources for proactive optimization ii. Complemented by local situational awareness (of peer / mesh / network state) for reactive control and resilience to unpredictable changes iii. Leverage metadata from content requests (e.g., delivery deadlines) d. Purpose and Principles i. No single point of failure in runtime agents / services (e.g. AmI rendezvous) ii. Indicator / Actuator Ontology for Data-Driven Innovation in Reinforcement Learning iii. Open framework with pluggable programming APIs iv. AI Agent Reuse and Market Enablement v. Enabling reuse of AI agents / components by innovative developers. vi. Interoperability with existing telco, ISP, CDN, and Internet networks, which may use existing standards such as OpenFlow or new AI interoperability standards evolved from the SPAN-AI ontology vii. Thus forming an interoperable mesh of intelligent networks, operating as a single, unified content delivery network with no single point of failure. 2. AI-HARD Operation (Intelligent Hybrid Adaptive Routing) a.AI-HARD enables networks to adaptively operate as a full p2p overlay, a full network-level mesh, or anything in between. b. Optimized role assignment and resource allocation i. Heterogeneous Agents / Roles 1) The algorithm determines: a) Service roles: DHT routing, name-based routing, storage, caching, discovery, monitoring, information reconciliation / decision-making b) Intelligence capabilities: reactive / proactive, local / global view, learning / observation, heuristic / optimization 2) Based on the following a) Network architecture level: peers, gateways, servers, switches, routers, application servers, etc. b) Trust, security and stability levels ii. Resource Allocation 1) The algorithm determines: a) Allocation of CPU, memory, disk, and upload / download bandwidth resources to each subsystem within each agent 2) Based on the following a) Service Role b) Intelligence Function c) Network Architecture Level d) Trust, security and stability levels 3) More stable endpoint nodes tend to allocate more resources to the storage-centric subsystem. 4) Nodes in the network with low stability tend to allocate more resources to the distribution-centric subsystem. 5) DHT replication levels are relaxed as name-based routing is enabled for delivery-centric applications. 6) DHT handles content with looser delivery requirements, which relaxes the name-based routing forwarding requirements. iii. Incentivize resource contributions via FIL-type marketplaces (see Market Enablement section) iv. Mostly provided by ambient and global intelligence nodes with edge / core level views / capabilities v. Complemented by self-role assignment and resource assignment functions c. Optimized long-term placement and short-term caching i. Distributed Cloud Network Flow Algorithm for Long-Term Placement in Distributed Storage-Centric Subsystems ii. Probabilistic local caching policies for delivery-centric subsystems d. Adaptive Name Resolution Routing vs. Name-Based Routing i. An AI-based optimization and self-organization method determines how the application splits requests between the two subsystems 1) Requests with loose delivery deadlines and requests with hard, predicted or pre-defined delivery deadlines can be handled by a "slower" but persistent and scalable DHT subsystem. 2) Requests with tight delivery deadlines are handled through a fast name-based routing subsystem. 3) Incentivize pre-planned requests via a marketplace like FIL (e.g. for large releases - see the Marketplace Enablement section) ii.DHT routing operation 1) A Kademlia-type protocol, but with improvements to multi-level DHT 2) Optimization of adaptive DHT parameters (nodes, blocks, replication factor, concurrency factor) 3) AmI rendezvous for DHT maintenance and self-organization under high churn (see AmI rendezvous operation) iii. Name-based routing operation 1) It is an NDN-type method, but reduces forwarding state by cooperating with the DHT subsystem. 3. AmI Rendezvous Operations (Intelligent Discovery and Configuration) a. Ambient Intelligence (AmI) refers to the combination of awareness and control for: i. Peer / Local Intelligence: Actuators embedded in pub / sub peers control the mesh's probability weights, degree and fanout. P2P pub / sub messaging observers compile metrics from neighboring subscriptions and events to infer health (e.g., hop count, reliability, latency, load balancing). Self-healing strategies can be as simple as filtering. ii. Edge / Swarm Intelligence: AmI health classification decisions (scoring, ranking) of peer and p2p overlay meshes are derived by basic reinforcement learning models. iii. Core / Global Intelligence: Maintains aggregated usage forecasts and mesh / network state. Determines rendezvous server placement. The b.AmI rendezvous service builds on state-of-the-art rendezvous services such as libp2p rendezvous, which supports periodic peer re-registration, discovery, bootstrapping, and is extended with peer heartbeats and mesh health metrics and rankings. c. Pluggable interface for self-healing agents to incorporate AmI rendezvous clients into pub / sub i. Incorporate pluggable indicators, actuators and triage for smart discovery ii. Periodic heartbeats distribute mesh health metrics and change deltas to the AmI rendezvous and SPAN-AI data lakes iii. Registration and re-registration is extended to exchange snapshots of the entire mesh d. Pluggable interfaces integrate smart discovery at rendezvous points The i.AmI rendezvous server and messaging is built on top of the libp2p rendezvous service, and metrics collection can be extended with time-series based monitoring systems such as Prometheus' InflUxDB. ii. Integrate a reinforcement learning agent for mesh health classification iii. Discovery is enhanced with peer rankings e. Additional features include discovery records, federation and caching, adaptive control, indicator / actuator reuse, topic-specific / device-specific indicators f. Further embedded intelligence and adaptive control in the simulation and development pipeline by integrating with AI-HARD solutions to support: i. Hybrid P2P routing via rendezvous bypass to meet deadlines or optimize overlay width ii. Assignment / deployment of rendezvous and information broker roles; iii. DHT topology layer, partitioning by naming enhancement or global recognition hints, iv. Coarse-grained adaptive control by activating plugins from a family of plugins, smart discovery / repair for DHT and NDN routing g. Support various mesh types (NBR, DHT-Kademlia) and guarantee requirement indicators (security, trust, integrity, efficiency, reliability, stability, latency).

[0301] SPAN-AI Simulator

[0323] SPAN-AI uses a simulation, training and development pipeline that enables cloud-level replication of runtime environments, simulation, testing and training of AI models and agents, which can be plugged into peer / edge / core / other network nodes for real-time optimization and control.

[0302]

[0324] SPAN-AI's simulator, the Self-Aware Mesh Simulator (SAMSim), is supported by: -Distributed cloud hosting a mesh big data lake with health metrics -Simulate and deploy AI models across an automated software engineering pipeline 1. Simulator Intelligence a. The test harness uses SPAN-AI intelligence for adaptive scalability, including guiding the placement of AmI rendezvous servers, scheduling and supporting data exchange between federated rendezvous servers and the data lake. b. Test frameworks are used for prototyping SAMSim, especially agile container infrastructure (e.g., HashiCorp Nomad and Consul for orchestration, Prometheus and InfluxDB for metrics). c. AI developers use mesh health metrics to test, train, sample simulations, and reward reinforcement learning. i. Mesh health metrics and actuators constitute a reinforcement learning problem. ii. Indicators and actuators are improved through rule derivation and management in the data lake. iii. The agent is refined from the rule set, indicators and actuators as follows: 1) Mock control and protocol plugins for abstract simulators (e.g., PeerSim, agent-based simulation, TestGround topology simulator, existing Gerbil simulators) 2) Prototype plugins for concrete embedded simulations (borrowed from D-P2P-Sim, RealPeer, ProtoSim) that are distributed to cloud providers such as Amazon Web Services (AWS) or Digital Ocean Cloud (DO-Cloud). iv. Data engineering tools are integrated for dataset preparation and quality control. d. Simulator and Pipeline Features: i. Mostly derived from integrating a test framework with an integrated P2P simulator ii. Includes multithreading, execution control / runners, realistic stub and network models, agile container infrastructure, metrics and visualization. e. Additional simulators include NS3 for network events, RealPeer and ProtoSim for improved models, PlanetSim for intelligent swarming, and PEERFACTSIM.kom for DHT routing. f. Additional simulator and pipeline features include customizable API standards, simulator layering, topology graph export formats, and fault simulation. 2. Pub / Sub Simulation a. Supports essential scaffolding for designing various trial agents by evaluating and integrating features from testing frameworks and third-party p2p simulators (e.g., PeerSim, D-P2P-Sim). b. Agent Engineering and Assurance Environment: i. Initial focus on ensuring latency with a scalable and resilient pub-sub mesh ii. Support simulation and iterative development of: 1) Triage for smart discovery with health indicator compilation, broadcast and gossip that triggers actuators to filter or limit or weight the probability of mesh overlay. 2) AmI Rendezvous Registration, Heartbeat, and Smart Discovery Messaging 3) Pub / sub bootstrap, subscription / publication, graft / purge messaging iii. Supports reuse of mesh health data sets and health indices and repair actuators across empirical simulation experiments, which may be expanded in future embodiments.

[0303] in use SPAN-AI Preferred Embodiments and Use Cases

[0325] In certain embodiments, a SPAN-AI embodiment may include a distributed origin store, publishing and distribution system using SPAN-AI, where the distributed video origin store and distribution service includes the following steps: 1. Video capture: encoding; packaging; encryption; signing 2. Assign a unified name 3. Publish to the distributed storage network (NRR and DHT) using storage metrics 4. Published in universal pub / subsystem using delivery QoS metrics.

[0304]

[0326] This embodiment of the use allows users to subscribe to videos using the universal pub / subsystem.

[0305]

[0327] This use embodiment also allows publishers to distribute videos using the NBR network for real-time (live) streaming and / or the NRR network for near / non-real-time delivery.

[0306] SPAN-AI for Gaming

[0328] While the preferred embodiment of SPAN-AI is for the distribution of video, it should be understood that SPAN-AI is designed as a Unified Content Delivery Network (UCDN) for any type of content, including but not limited to: game streaming (distributed or from a "server" or from a consumer's device); distributed game execution; social media; websites; blogs; e-commerce; medical applications such as MRI, X-ray, remote diagnostics; simulation; command and control; etc. i. http: / / citeseerx.ist.psu.edu / viewdoc / download?doi=10.1.1.366.6736&rep=rep1&type=pdf ii. https: / / named-data.net / iii. https: / / wiki.fd.io / view / Cicn iv. https: / / trac.ietf.org / trac / irtf / wiki / icnrg v. https: / / ipfs.io / < / cid>

Claims

1. A network device capable of operating in conjunction with a plurality of similar network devices, the network device receives digital content from a remote location; the network devices receive digital content from remote locations including databases stored in or forming part of an origin store, or databases that are separate servers, or databases that are stored at least partially in memory of individual network devices to provide a distributed storage configuration; the network appliance includes decoding and re-encoding means by which digital content is downloaded, decoded, and then re-encoded for transmission to a digital device for consumption by a user; each of said network devices forms an intelligent node in a mesh network; Each of the network devices communicates through the mesh network by referencing a network address; Each network device operates in accordance with secure peer-assisted routing standards, including the use of secure protocols for data transmission; the secure peer-assisted routing criteria enable receiving at least a portion of an item of digital content from another network device of the plurality of network devices if the item of digital content has previously been downloaded to the other network device of the plurality of network devices; each said network device receiving said digital content from a network address from which the next digital bit or group of bits can be most easily and efficiently obtained to maintain real-time or near real-time delivery of said digital content; the digital content is recorded according to a secure encoding algorithm; Each of the network devices is a. Most wanted packets b. Fastest download speeds c. Minimum latency d. receiving the digital content according to the secure peer-assisted routing criteria, including one or more of: a network address from which the next digital bit or group of bits can be most easily and efficiently obtained in order to maintain real-time or near real-time delivery of the digital content; said next digital bit or group of bits based on distributed and / or centralized routing information in the form of a hash table or other efficient database mechanism; a. Local network device feeds; b. Peer feed; and c. Server Feed and the routing information includes address information for all data packets forming the digital content; The secure peer-assisted routing standards and application programs based thereon recognize and report network traffic at the SCTP, TCP / IP, UDP and video packet level, and each network device forms an intelligent node in a mesh network described as grid computing or distributed cloud computing; The Secure Peer-Assisted Routing standard combines distributed and centralized routing information and intelligence down to the video packet level to enable optimal management of networks with software-defined network-like performance.

2. 1. A distributed system for distributing digital content, the system comprising: at least one content aggregator and a plurality of network devices in communication with the origin store; the aggregator receiving digital content in the form of an item of content; the aggregator protects the digital content for distribution by the system; the origin store making the digital content available to the plurality of network devices; each network device receiving the identified item of content in response to a request by said network device to said system; the network equipment operates in accordance with secure peer-assisted routing standards, including the use of secure protocols for data transmission; the secure peer-assisted routing criteria allows receiving at least a portion of the item of content from another network device of the plurality of network devices if the item of content has previously been downloaded to the other network device of the plurality of network devices; the network devices receive digital content from remote locations including databases stored in or forming part of an origin store, or databases that are separate servers, or databases that are stored at least partially in memory of individual network devices to provide a distributed storage configuration; the digital content is recorded according to a secure encoding algorithm; Each of the network devices is a. Most wanted packets b. Fastest download speeds c. Minimum latency d. receiving the digital content according to the secure peer-assisted routing criteria, including one or more of: a network address from which the next digital bit or group of bits can be most easily and efficiently obtained in order to maintain real-time or near real-time delivery of the digital content; said next digital bit or group of bits based on distributed and / or centralized routing information in the form of a hash table or other efficient database mechanism; a. Local network device feeds; b. Peer feed; and c. Server Feed and The secure peer-assisted routing standards and application programs based thereon recognize and report network traffic at the SCTP, TCP / IP, UDP and video packet level, and each network device forms an intelligent node in a mesh network described as grid computing or distributed cloud computing; The Secure Peer-Assisted Routing Standard combines distributed and centralized routing information and intelligence down to the video packet level to enable optimal management of networks with software-defined network-like performance; This provides comprehensive quality of service (QoS) monitoring and control across the network.

3. The system of claim 2 , wherein the system communicates over the Internet.

4. The system of claim 2 , wherein the device is implemented on a circuit board that includes at least a trusted platform module (TPM) that communicates with a processor and memory.

5. The system of claim 4 , wherein the TPM may be embodied in the processor or an associated system module.

6. The system of claim 4 , wherein the trusted platform module includes a unique identifier, a certificate for encryption and decryption, and secure boot code.

7. The network device of claim 1 , wherein the network device receives the additional protection data from an independent server.

8. 10. The network appliance of claim 1, implemented using a virtual machine environment that provides direct hardware access to an operating system.

9. 9. The network appliance of claim 8, wherein the operating system is Windows 10 tightly integrated with an Intel processor hardware platform that implements PlayReady in hardware under the Microsoft Windows 10 (or later) operating system.

10. The system of claim 2 , wherein the secure peer-assisted routing criteria is inserted into or integrated with an adaptive bitrate protocol.

11. The system of claim 10 , wherein the adaptive bitrate is converted to progressive download or optimal fixed rate streaming depending on available user bandwidth.

12. The system of claim 2 , wherein the secure peer-assisted routing criteria is integrated with dynamic adaptive streaming over HTTP.

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