TECHNICAL FIELD

[0001] The present disclosure generally relates to heuristic learning channels. More particularly, the disclosure relates to a linear content aggregator system (LCAS) system which provides automatic learning channels which is customized for a particular viewer and a method of creating the same is disclosed.

BACKGROUND OF THE INVENTION

[0002] This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described below. This disclosure is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not just as admissions of prior art.

[0003] Television viewers have been watching television programs for more than sixty years. In the beginning, a small number of television programs were broadcast over the airwaves, and users used to select a channel among those broadcasted programs/channels. Over time, more television viewers have come to receive their television programs from a satellite network or a cable network. In addition to standard programming, the satellite and cable networks offer premium channels, pay-per-view programs, and a host of interactive programs.

[0004] A linear content aggregator system (LCAS) with x integrated receivers/decoders (IRDs) can serve a minimum of 120 channels from x different transponder frequencies. Such LCAS may be installed in village panchayats or any customer interested sites. But practically, at a given site location, not all these channels will be of interest to the population at the site location. Providing all the channels adds to the hardware requirements of the system and hence, adds to the cost.

[0005] If it is in rural locations, where the need of video channels limited to their language mostly, the hardware infrastructure required in such places can be optimized by providing only users interested channels. This may be true even when the system is installed in schools etc. where only educational channels will be of interest. [0006] In such scenarios, the interest of the consumer varies according to geographical location, language, age group, and other demographic parameters. Hence, we understand that all the video channels may not be viewed in a given location. This shows the need of optimization on LCAS based on heuristics to serve the user interested channels, instead of 120 channels from all IRDs. Thereby, reducing the load on either digital video broadcasting (DVB) receivers, a video server, and a media server. Although it is not possible to accurately know apriori, the channels of interest in a given site location, statistically some assessment can be done to determine possible channels of interest. Reference may be made to US2012/0084801A1 which enclosed a system and method for providing real time television viewing information and popularity to viewers. Reference may be made to US 9264775 B2 which enclosed systems and methods for managing data in an intelligent television. Reference may be made to US 9781476 B2 which enclosed automatic learning channel customized to a particular viewer and method of creating same.

[0007] In the light of aforementioned challenges, there is a need for a novel, less complex selflearning system to finalize the channels of interest. In this disclosure, a method to dynamically decide on the channels of interest is described, in order to optimize the hardware investment on the system. The optimization results in cost effective LCAS where a single physical server of comparatively lower capacity can act as the video server and the media servers.

SUMMARY OF THE INVENTION

[0008] The disclosure provides a novel and a simple linear content aggregator system (LCAS) for providing channels of interest to a user.

[0009] In one aspect, a method for providing channels of interest to a user is disclosed herein. The method comprising the steps of selecting a list of channels from a plurality of channels based on a demographic parameter and serving the selected list of channels for a predetermined threshold. The method further comprising the steps of collecting viewership statistics of the serving list of channels to provide a popularity identifier and retaining a subset of channels from the list of channels based on the popularity identifier. The method further comprising the steps of combining another subset of channels from the plurality of channels with the retained subset of channels to form a new set of channels and providing a new set of channels to the user and repeating the steps of serving, collecting, retaining, and combining for the new set of channels till all the channels in the plurality of channels are served and a preferred set of channels is selected for the user.

[0010] Optionally, the demographic parameter comprises one or more of a geolocation, a language, an age group, and other demographic parameters.

[0011] Optionally, the predetermined threshold comprises any number of days between 1-90.

[0012] Optionally, the viewership statistics of the channels comprises cumulative count of number of times each channel of the selected list of channels being watched by users and cumulative time period of each channel of the selected list of channels being watched by the users.

[0013] Optionally, the popularity identifier is provided by: calculating a weighted average value of each channel using weights wl, w2 and comparing the weighted average value of each channel against a decision threshold to decide whether the channel is popular or not, wherein the channel having the weighted average value of the data point greater than the decision threshold is the popular channel.

[0014] Optionally, selecting the list of channels comprises selecting the list of channels such that the users are offered with a good number of channels of interest.

[0015] Optionally, serving the new set of channels for a predetermined threshold; collecting viewership statistics of the serving new set of channels to provide a popularity identifier; retaining a subset of channels from the new set of channels based on the popularity identifier; combining another subset of channels from a plurality of channels with the retained subset of channels to form a newer set of channels; and providing the newer set of channels to the user and repeating the steps of serving, collecting, retaining, and combining till all the channels in the plurality of channels are served and a preferred set of channels is selected for the user.

[0016] Optionally, adding a plurality of specific channels from a particular genre into the list of channels for analysis in a particular time window.

[0017] In another aspect, a linear content aggregator system (LCAS) is disclosed herein. The LCAS comprising a plurality of integrated receiver/decoder (IRD) units configured to receive digital video broadcasting (DVB) satellite complaint contents from an antenna, wherein each IRD unit comprises a tuner configured to lock to a specific frequency, and the IRD units further configured to demodulate and decode the DVB satellite complaint contents to a multi program transport stream (MPTS). The LCAS further comprises of a video server configured to receive the MPTS from the plurality of IRD units, wherein the video server is configured to demultiplex each MPTS into individual channels and a media server configured to receive the individual channels using real-time messaging protocol (RTMP) from the video server, and convert the individual channels to dynamic adaptive streaming over HTTP/HTTP live streaming (DASH/HLS) compatible channels. The LCAS further comprises of a web server configured to receive the DASH/HLS compatible channels and provide the channels to mobile devices over Wi-Fi, wherein the web server is further configured to: select a list of channels from a plurality of channels based on a demographic parameter; serve the selected list of channels for a predetermined threshold; collect viewership statistics of the serving list of channels to provide a popularity identifier; retain a subset of channels from the list of channels based on the popularity identifier; combine another subset of channels from the plurality of channels with the retained subset of channels to form a new set of channels; and provide a new set of channels to the user and repeating the steps of serve, collect, retain, and combine for the new set of channels till all the channels in the plurality of channels are served and a preferred set of channels is selected for the user.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To further clarify advantages and features of the present disclosure, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting of its scope. The disclosure will be described and explained with additional specificity and detail with the accompanying drawing in which:

[0019] Figure 1A illustrates a schematic diagram of a LCAS in accordance with an embodiment of the disclosure;

[0020] Figure IB illustrates a schematic diagram of LCAS IRDs in accordance with an embodiment of the disclosure;

[0021] Figure 1C illustrates a schematic diagram of video server in accordance with an embodiment of the disclosure;

[0022] Figure 2 illustrates a schematic flowchart of LCAS Network Optimization based on heuristics in accordance with an embodiment of the disclosure;

[023] Figure 3 illustrates a schematic flowchart of a self-learning algorithm in accordance with an embodiment of the disclosure; and [0024] Figure 4 illustrates another schematic flowchart representing embodiments of the selflearning algorithm in accordance with an embodiment of the disclosure.

DETAILED DESCRIPTION OF THE INVENTION

[0025] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

[0026] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof. Throughout the patent specification, a convention employed is that in the appended drawings, like numerals denote like components.

[0027] Reference throughout this specification to “an embodiment”, “another embodiment”, “an implementation”, “another implementation” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase “in an embodiment”, “in another embodiment”, “in one implementation”, “in another implementation”, and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0028] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures proceeded by “comprises” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or additional devices or additional sub-systems or additional elements or additional structures.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The apparatus, system, and examples provided herein are illustrative only and not intended to be limiting.

[0030] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the terms control panel, control unit, and power control unit denotes the same meaning and may be used interchangeably throughout the description.

[0031] Embodiments of the disclosure will be described below in detail with reference to the accompanying drawings.

[0032] Figure 1A is a schematic diagram of a linear content aggregator system (LCAS) 100 in accordance with an embodiment of the disclosure. The LCAS 100 is a television content delivery platform which receives the digital video broadcasting - satellite/second generation (DVB-S/S2) television (TV) signals, demodulates, decodes, and streams the channels over wireless fidelity (Wi-Fi) access point 190 to mobile devices 195 as shown in Figure 1A. The mobile users can watch the content on mobile browsers by accessing a web server 140. Moving picture experts group-dynamic adaptive streaming over HTTP (MPEG-DASH) and HTTP live streaming (HLS) are the widely used HTTP based video streaming protocols to view the video content on mobile browsers. The LCAS 100 provides the content using both the MPEG-DASH and the HLS protocols to support large number of mobile devices 195.

[0033] The LCAS 100 includes an integrated receivers/decoders (IRDs) unit 110, a video server 120, a media server 130, and a web server 140. The IRDs unit 110 results in a minimum of 120 channels approximately, as the IRDs unit 110 provides content of multiple satellite transponders. Further, the video server 120, the media server 130, and the web server (i.e., LCAS server) 140 processes all the channel’s data and serve the content to the mobile users over the Wi-Fi. The LCAS servers 140 consumes a lot of memory and CPU to process all the channels. Hence, the LCAS servers 140 require a high-capacity server which results in higher cost hardware.

[0034] A satellite television is a broadcasting platform where television signals are transmitted from Earth via DVB-S/S2 satellite 160 and received by a parabolic reflector dish antenna (i.e., DVB-S/S2 antenna) 170 on the ground, as shown in Figure 1A. The satellite television system comprising the DVB-S/S2 satellite 160 which beams down the television signals from the DVB-S/S2 satellite 160 to the Earth. The DVB-S/S2 satellites 160 are placed 22,000 miles above the equator in geostationary orbits. [0035] The DVB-S/S2 satellites 160 in geostationary orbit orbits the Earth at the same rate the Earth rotates. Hence, the DVB-S/S2 satellites 160 appears to be static to the uplink dish antenna (not shown in Figure 1A). The DVB-S/S2 satellite 160 receives the television signals, uplinked by dish antennas from the ground. The uplink satellite dish antennas are huge in size approximately 9-12 meters in diameter, so as to receive signal with good strength at the DVB- S/S2 satellite 160. The uplink antenna transmits the signals in a specific frequency range, usually in C-band (4 to 8 GHz) directing towards the DVB-S/S2 satellite 160. The DVB-S/S2 satellite 160 consists of on-board transponders. Each transponder is configured for a specific frequency range. The transponder receives the signals uplinked by the dish antennas in its frequency range. The on-board transponder downlinks the television signals back to the Earth in a different frequency range, usually in Ku band (10.7 to 12.7 GHz), to avoid interference with the uplink signals. The process of converting signals from one frequency to another frequency is called translation. The typical bandwidth of each transponder aboard the satellite is 27 to 50 MHz. Each transponder can carry 20 standard definition (SD) channels of MPEG2 compression approximately or 40 SD channels of MPEG4 compression. The transponder supports a smaller number of high definition (HD) channels compared to SD.

[0036] The DVB-S/S2 antenna 170 on the ground at the user location receives the downlinked signals from the DVB-S/S2 satellite 160 as shown in Figure 1A. The receiving antennas (i.e., DVB-S/S2 antenna 170) are smaller in size compared to the uplink dish antennas, as the downlinked signals are in higher frequency (Ku band) i.e., lower wavelength compared to uplink frequency i.e., C-band. The metallic parabolic reflector antenna (i.e., DVB-S/S2 satellite 160) receives the maximum signal and reflects to a feed horn mounted at focal point of antenna. The feed horn sends the signal to low noise block downconverter (LNB) 172 (as shown in Figure IB). The LNB 172 sends the amplified signals to the IRD unit 110 through a coaxial cable. The LNB 172 has two major functions: the signals received at antenna are extremely attenuated after traveling 22,000 miles. Hence, the LNB 172 amplifies the received signal. The second function of the LNB 172 is to down convert the signal to intermediate frequency (IF) usually to L band (1-2 GHz). Expensive metallic waveguides are required to carry Ku band signals whereas low-cost coaxial cables are sufficient to carry low frequency L-band signals.

[0037] The IRDs unit 110 consists of set of IRD hardware devices. The functionality of the IRDs unit 110 is to receive the DVB-S/S2 signal, demodulate, decode and stream to video server 120. A tuner 112 aboard the IRDs unit 110 receives the television signals of a specific IF in L-band from the LNB 172 through coaxial cable (RG6 CCS), as shown in Figure IB. The tuner 112 passes the signals to a demodulator 114. The demodulator 114 demodulates the L- band signals to baseband. The baseband signal contains multiple DTV and/or radio channels in it which is called multi program transport stream (MPTS). Each IRD tuner 112 can receive TV signals of a specific frequency in L-band. The LCAS 100 consists of x (~8) such IRDs unit 110 approximately, which receives television signals of x different frequencies. Each frequency supports y ( >15) number of TV channels. Hence, the IRDs unit 110 consisting of x IRDs can provide a minimum of x*y ( >120) channels approximately. The x*y channels of x MPTSs from x different IRDs are streamed to video server 120 as user datagram protocol (UDP) packets over Ethernet via an Ethernet switch 150 for further processing.

[0038] The video server 120 is a software module whose primary functionality in brief is to demultiplex the input MPTSs 122 into individual channels, transcode, and stream as RTMP packets 129 to media server 130, as shown in Figure 1C. The media server 130 converts input streams to DASH/HLS compatible streams. The video server 120 handles following functions:

• Receives MPTSs streams 122 on UDP from IRDs 110 over Ethernet.

• De-multiplexes the MPTSs into individual channels 124.

• Transcodes each de-multiplexed channel with H.264 codec 126.

• Converts each channel’s container format to flv 128.

• Streams out the channels to media server 130 over RTMP 129.

[0039] The video server 120 receives x*y ( >120) number of channels of x MPTSs of x different L-band frequencies from x IRDs as UDP packets over Ethernet. The purpose of LCAS 100 is to serve individual channels to users rather than MPTSs. Hence, demultiplexer of video server 120 de-multiplexes the MPTS 122 into SPTSs 124 i.e., individual channels as shown in Figure 1C. The LCAS 100 serves only a fraction of the total number of channels, considering cost of hardware resources to process all the x*y number of channels, which is core idea of this patent. Hence, the video server 120 demultiplexes z (30 approximately) number of channels which is only a fraction of total x*y, which are to be served to the users. An algorithm is devised to filter out most popular z number of channels from x*y, based on viewership statistics.

[0040] The de-multiplexed SPTSs are with native codecs which can be any. Even though MPEG-DASH is codec agnostic, the HLS doesn’t support all the codecs. The HLS supports H.264/AVC and H.265/HEVC codecs only. Hence, “transcoder” of the video server 120 transcodes SPTSs with H.264 codec which is widely used across many streaming platforms. “Video container formatting” block of the video server 120 modifies the container format of demultiplexed SPTSs from .is to .flv to conform with RTMP streaming.

[0041] The de-multiplexed, transcoded, and /Zv container encapsulated individual channels are streamed to the media server 130 as RTMP packets over Ethernet. The RTMP is the common streaming protocol agreed between video server 120 and the media server 130. RTMP is a TCP based communication protocol. Communication latency is low in RTMP.

[0042] The purpose of LCAS 100 is to deliver the video content without any external software package installation at user’s end. This is achieved by streaming the channels over HTTP so that the end users can watch the content in mobile browsers without installing any additional software packages or plug-ins. The most popular HTTP based streaming protocols are MPEG- DASH (Dynamic Adaptive Streaming over HTTP) and HLS (HTTP Live Streaming).

[0043] The media-server 130 receives individual channels in FLV containers over RTMP. It then generates DASH/HLS compatible streams. The MPEG-DASH is widely used, HTTP based video streaming protocol. It is codec agnostic that means video can be compressed with H.264, H.265 or any other codec. It doesn’t support Apple devices. It is ISO open-source standard. It works by breaking down the input content into sequence of smaller fragments/segments/chunks (,m4v, ,m4a) of specified time duration.

[0044] The fragments are usually 2 or 4 seconds long videos, encoded with various quality levels so that right quality segments will be served based on network signal strength of the end user. This is called Adaptive Bit Rate (ABR) streaming which is a key feature in MPEG-DASH. It also generates a manifest/playlist/MPD (Media Presentation Description) file along with the fragments. Manifest is a text file which describes the characteristics of fragments like URL, video resolution, bit rate etc. The manifest file delivered to web player usually via Content Delivery Network (CDN). The web player requests the appropriate fragment files from manifest based on network signal strength. The current implementation delivers manifest file to web player via the web server 140.

[0045] The HLS is also widely used HTTP based video streaming protocol, similar to MPEG- DASH. The HLS is proprietary protocol of Apple Inc. It also works in a similar way to MPEG- DASH by breaking down the input video into smaller fragments (.ts) and generating manifest (.m3u8) file. The manifest file is delivered to player via web server 140. The HLS supports both apple and android devices. The HLS isn’t codec agnostic. It supports only H.264 and H.265 codecs. The HLS is proprietary protocol of apple, not open-source standard. MPEG- DASH isn’t supported in Apple devices such as AppleTv, iPhone and iPad where safari is the default browser. The HLS is supported in Android, MAC, iOS, Windows, Linux and many other platforms also. Hence, the HLS has support in various devices compared to MPEG- DASH.

[0046] The LCAS 100 provides the DVB-S/S2 TV channels using both MPEG-DASH and HLS protocols. The media server 130 accepts individual channels as an input over RTMP and generates DASH, HLS fragments/segments and manifest/playlist files. The manifest files are served to web player via a web server 140.

[0047] The users can access the web server 140 and watch the channels in mobile browsers. An embedded HTML-5 web player processes the manifest file, fetches the segment files and plays them as a continuous video stream.

[0048] As shown in Figure 1A, the LCAS 100 provides the channels to mobile users over WiFi. The users watch these channels on mobile browsers through a website hosted over the web server 140. This web server 140 can collect the statistics of channels viewership based on requests received from users. The viewership statistics data is analyzed and processed to identify popular channels via an algorithm.

[0049] The LCAS 100 may be deployed at a local governing body, mall, school, or any customer interested crowd gathering sites. Interests of the users vary according to many parameters for example, regional language, age group, literacy percentage, rural, urban etc. Educational institutions may be interested in only educational channels, countryside users may be interested in channels of local language only. Users in any demographic region may not be interested in all the channels. Hence, the LCAS 100 can be optimized to serve, only users interested i.e., popular channels in the deployed region. A low-capacity server hardware of low cost, suffices for the optimized LCAS 100, as popular channels are only served instead of all the channels. An algorithm is proposed in this disclosure to optimize the LCAS 100 by identifying the popular channels.

[0050] The LCAS optimization algorithm starts with serving a set of channels. The initial list of channels is selected by assuming reasonable criterion e.g., regional language, age group etc. As shown in Figure 2, the web server 140 collects viewership statistics of these channels based on user’s requests i.e., watching behaviour. The statistics data is processed to select the popular channels from the served. The list is further updated with the unserved channels and statistical data is collected on every updation of the serving channels list. The process is continued until all the channels are served once. The cycle is repeated to average out the statistics over the time. Thus, the LCAS optimization algorithm results in most popular channels out of the all channels from all transponders.

[0051] In particular, Figure 3 describes a method, performed by the LCAS 100 or the web server 140, to dynamically decide on channels of interest. The method uses a self-learning system to finalize the channels of interest. The self-learning system selects S301 a list of channels (e.g., 30 interested channels) from a plurality of channels (e.g., 120 total channels) based on a demographic parameter. In an exemplary embodiment, the self-learning system may be initially configured with a list of channels (e.g., 30 interested channels). These 30 interested channels are selected such that viewers are offered with a reasonably good number of channels of interest as per their geographical location, language, age group, and other demographic parameters. The interested channels list is updated dynamically based on a self-learning method.

[0052] Further, the self-learning system serves S302 the selected list of channels (i.e., 30 interested channels) for a predetermined threshold. In an exemplary embodiment, the predetermined threshold would be any number of days between 1-90.

[0053] The self-learning system collects S3O3 viewership statistics of the serving list of channels (i.e., 30 interested channels) to provide a popularity identifier. In particular, the viewership statistics of the serving list of channels (i.e., 30 interested channels) in the predetermined threshold (i.e., any number of days between 1-90), may provide a number of times or the time period of watching a channel.

[0054] The self-learning system retains S304 a subset of channels (e.g., 15 most viewed channels) from the list of channels (i.e., 30 interested channels) based on the popularity identifier. Additionally, the self-learning system combines S305 another subset of channels (e.g., 15 new channels and other than the previously selected 30 interested channels) from the plurality of channels (i.e., 120 total channels) with the retained subset of channels (i.e., 15 most viewed channels) to form a new set of channels (i.e., 15 most viewed channels + 15 new channels). In particular, 15 most viewed of the 30 interested channels are retained in the list and the remaining 15 channel list gets updated gradually from the unserved channels.

[0055] Furthermore, the self-learning system provides S306 a new set of channels to the user and repeats S307 the steps of serving, collecting, retaining, and combining for the new set of channels (i.e., 15 most viewed channels + 15 new channels) till all the channels in the plurality of channels (i.e., 120 total channels) are served and a preferred set of channels is selected for the user. In particular, this process is repeated until the channels from the plurality of channels (e.g., 120 total channels) are served. This results in viewership statistics of all channels and the LCAS 100 or the web server 140 serves S3O8 preferred set of channels to the user. Eventually, one cycle of the above steps results in the final list of most viewed 30 channels over that period of time.

[0056] Additionally, it is important to note that some specific channels from a particular genre might be explicitly pushed into the list of channels (i.e., 30 interested channels) for analysis in a particular time window. In particular, sports or news channels might have seasonal upticks in response to major sports or political events etc. Hence, during this major sports or political events, the sports or news channels might be pushed into the list of channels (i.e., 30 interested channels) for analysis. Furthermore, the number of algorithm cycles can be increased for factoring-in different time-windows over which the data is analyzed for cross validation.

[0057] In an embodiment, the demographic parameter may comprise one or more of a geolocation, a language, an age group, and other demographic parameters.

[0058] In an embodiment, the viewership statistics of the channels may comprise cumulative count of number of times each channel of the selected list of channels being watched by users and cumulative time period of each channel of the selected list of channels being watched by the users.

[0059] In an exemplary embodiment, as shown in Figure 4, the LCAS 100 or the web server 140 collects S401 the viewership data based on requests received from the users. The LCAS 100 or the web server 140 processes S402 the viewership data, where the viewership data contains the 2 features -.feature 1- cumulative count of how many times each channel is watched by all users, and feature 2-cumulative time period (i.e., in seconds) of each channel watched by all users. The final data set size is (x*y, 2).

[0060] Further, as shown in Figure 4, an algorithm assigns a probabilistic weight to each of the features say wl (<1) Cofeature 1 and w2 (<1) lofeature2 as both haven’t been assigned equal weight for this specific embodiment. Identifying thresholds of 2 parameters. It finds a threshold for the parameter 1 after analyzing the complete data of parameter 1. It represents average view count of each channel. The threshold value of it is ml. [0061] Similarly, it also finds a threshold for parameter 2 after analyzing the complete data of parameter 2. It represents average time period of watching a channel. The threshold value of it is m2.

[0062] Then, it finds the final decision threshold value of both the parameters as mentioned below

Decission Threshold (Th') = wl * ml + w2 * m2

[0063] The LCAS 100 or the web server 140 calculates S404 the weighted average value of each data point using weights wl, w2. Let say it will be di, d2 . . . d _xy . Further, the LCAS 100 or the web server 146 compares weighted average value of each data point against the decision threshold i.e., Th to decide whether the channel is popular or not. If any channel’s weighted average of data point is greater than the threshold, it will be considered as popular channel S408, and if any channel’s weighted average of data point is less than or equal to the threshold, it will be considered as popular channel S410. Hence, this is a binary classification algorithm. These channels are provided to video server 120 for further processing and to serve users.

[0064] Thus, end of this algorithm results in popular channels out of the served channels, for that cumulative time period. The process is repeated until all the channels are served. The selflearning process ends with identifying p most popular channels out of x*y.

[0065] The foregoing descriptions of exemplary embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiment was chosen and described in order to best explain the principles of the disclosure and its practical application, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions, substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but is intended to cover the application or implementation without departing from the spirit or scope of the claims of the present disclosure.

[0066] Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

[0067] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the apparatus in order to implement the inventive concept as taught herein.