Abstract: A buffer model in an HTTP streaming client may include receiving a first content fragment of a first content stream in response to a first HTTP request. It may also include receiving a second content fragment of a second content stream in response to a second HTTP request. The buffer model may further include storing the first and second content fragments in first and second buffers of a plurality of configurable buffers. The first and second content fragments may be multiplexed into a third buffer of the plurality of buffers. The multiplexed first and second content fragments may be stored in a fourth buffer of the plurality of buffers for playback. The buffer model may be implemented by an application. The buffers may be designed based on one or more constraints.

Abstract: Techniques for influencing power consumption of a client while streaming multimedia content from a server over a network are described. For example, a server push strategy is used to push a number of media segments of the multimedia content from the server to the client in response to a single request identifying one of the media segments. Thus, instead of using multiple requests, the media segments are provided to the client by using a single request. Reducing the number of requests influences (e.g., reduces) the power consumption of the client. To optimize the power consumption given current client, server, and/or network conditions, the number of the media segments to be pushed is computed based on parameters associated with these conditions.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a BMC. The BMC stores an extended SEL record entry in a data repository. The extended SEL record entry including a locator to a data collection stored external to the data repository. The BMC further retrieves the data collection based on the locator in response to receiving a request-to-retrieve-an-SEL-record.

Abstract: Techniques for influencing power consumption of a client while streaming multimedia content from a server over a network are described. The power consumption is influenced by controlling how many requests are used by the client to receive media segments of the multimedia content from the server. For example, the number of requests is reduced by increasing the durations of the available media segments. Thus, rather than requesting a certain number of media segments each of a particular duration, a lower number of media segments is requested where the requested media segments have longer durations. Although a longer duration generally results in a larger power saving, the length of a requested media segment is balanced against other parameters associated with current conditions of the client, server, and/or network to allow for, for example, adaptive streaming. As such an optimum duration is determined based on different parameters associated with the current conditions.

Abstract: Embodiments of a system and method for local generation of streaming content with a hint track are described. Embodiments may include receiving a first version of encrypted content comprising encrypted content samples that each include media content and non-content information. Embodiments may also include receiving a hint track including packet header information for a stream of media packets from which the media content was sourced, and offset information identifying locations of encrypted media content within the encrypted content samples. Embodiments may include generating a second version of the encrypted content for streaming, which may include, based on the information of the hint track, identifying the location of media content within the encrypted content samples.

Abstract: Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.

Abstract: Embodiments may be configured to receive a protected version of content that includes multiple encrypted content samples. In various embodiments, each encrypted content sample includes multiple encrypted blocks. For a given encrypted content sample, different sets of encrypted blocks in that sample may form different encryption chains. The protected version of the content may further include decryption information for decrypting the encrypted content samples. The decryption information may include at least some initialization vectors generated dependent upon non-content information that is not included in the protected version of the content. The non-content information may be from a different protected version of the content. Embodiments may be configured to use the decryption information to decrypt one or more of the encrypted content samples.

Abstract: Systems and methods to control a controller, such as a baseboard management controller (BMC), and its host computer with voice. The system may include a mobile device, which is communicatively connected to the controller via a network. The mobile device may receive a voice input, and generate a corresponding instruction by performing voice recognition to the voice input. The instruction may be sent to the controller for controlling the controller or its host computer. For example, the controller may perform a corresponding operation based on the instruction to determine whether the instruction is for the controller or for the host computer. When the instruction is for the host computer, in response to determining the target as the host computing device, the controller may perform keyboard-video-mouse (KVM) redirection to redirect the instruction to the host computer.

Abstract: A buffer model in an HTTP streaming client may include receiving a first content fragment of a first content stream in response to a first HTTP request. It may also include receiving a second content fragment of a second content stream in response to a second HTTP request. The buffer model may further include storing the first and second content fragments in first and second buffers of a plurality of configurable buffers. The first and second content fragments may be multiplexed into a third buffer of the plurality of buffers. The multiplexed first and second content fragments may be stored in a fourth buffer of the plurality of buffers for playback. The buffer model may be implemented by an application. The buffers may be designed based on one or more constraints.

Abstract: This disclosure generally covers systems and methods that determine demographic labels for a user or a group of users by using digital inputs within a predictive model for demographic classification. In particular, the disclosed systems and methods use a unique combination of classification algorithms to determine demographic labels for users as a potential audience of digital content items. When applying the combination of classification algorithms, the disclosed systems and methods use a first classification algorithm to determine user-level-latent features for each user within a group of users based on demographic-label statistics associated with particular digital content items. The disclosed systems and methods then use the user-level-latent features and session-level features (from sessions of each user consuming the digital content items) as inputs in a second classification algorithm to determine a demographic label for each user within the group of users.

Abstract: Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and a computer system are provided. The computer system includes a service processor. The service processor monitors events of one or more ACPI compliant devices of a host of the service processor. The service processor maintains device data associated with the one or more ACPI compliant devices based on the events in a data store of the service processor. The service processor emulates an ACPI controller to monitor a communication channel for detecting one or more ACPI commands from the host. The service processor processes the device data in the data store in response to detecting the one or more ACPI commands on the communication channel.

Abstract: The present disclosure is directed toward systems and methods for identifying user segments. In particular, the systems and methods described herein evaluate user session logs to gather media content consumption history information associated with a plurality of users. Additionally, the systems and methods described herein analyze items of media content to identify keywords, genres, and other attributes, and further represent the items of media content as vectors. The systems and methods follow an algorithm to group items of media content into clusters and, based on the clusters of media content, further group users of media content into user clusters (e.g., user segments).

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a service processor. The service processor emulates a frame buffer. The service processor determines whether the service processor is configured to generate display signals to be sent to a display based on images from the service processor or based on images from a host of the service processor. When the service processor is configured to generate the display signals based on the images from the service processor, the service processor generates a first image based on serial data received from the host through a serial port, writes the first image to the emulated frame buffer, and generates a first display signal to be sent to the display based on the first image from the emulated frame buffer.

Abstract: Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.

Abstract: The present disclosure includes methods and systems for streaming high-performance virtual reality video using adaptive rate allocation. In particular, an adaptive rate allocation system partitions a panorama video into segments or tiles and assigns priorities to each tile or segment based on input (e.g., a viewport of field-of-view) from a user client device. Further, the adaptive rate allocation system streams each tile or segment to the user client device according to the adaptive rate allocation, which maximizes bandwidth efficiency and video quality. In this manner, the adaptive rate allocation system delivers higher quality content to regions in the panorama video where a user is currently looking/most likely to look.

Abstract: A pair of video streams is prepared for stereoscopic display. A pair of frames is rectified. Each of the pair of frames is from a respective stream of the pair of video streams. A reduced video stream is generated by removing from one of the pair of video streams a set of correlative data that is present in or can be predicted from the other of the pair of video streams. The reduced video stream and the other of the pair of video streams are compressed for use in stereoscopic display of the reduced video stream and the other of the pair of video streams.

Abstract: A key delivery mechanism that delivers keys to an OS platform (e.g., iOS platform) devices for decrypting encrypted HTTP live streaming data. An HTTPS URL for a stateless HTTPS service is included in the manifest for an encrypted HTTP live stream obtained by an application (e.g., a browser) on an OS platform device. The URL includes an encrypted key, for example as a query parameter value. The application passes the manifest to the OS. The OS contacts the HTTPS service to obtain the key using the URL indicated in the manifest. Since the encrypted key is a parameter of the URL, the encrypted key is provided to the HTTPS service along with information identifying the content. The HTTPS service decrypts the encrypted key and returns the decrypted key to the OS over HTTPS, thus eliminating the need for a database lookup at the HTTPS service.

Abstract: Techniques are disclosed for secure playback of protected multimedia content on a game console using a secret-less application. An SSO model can be used for client authentication at a key server, which eliminates the need of storing or using any secret information in the client application. Further, an encrypted content key generated by a content packager using a public key can be deployed in the key URI of a playlist file, which is sent to the key server. The key server can be configured to decrypt the content key using a corresponding private key. Further, the content key and unencrypted samples are protected in the game console client application from debugging and replay attacks by using additional security checks at both the client and key server. By storing secret information remotely from the game console and using the SSO model, DRM policies can be enforced on an untrusted client application.

Abstract: Systems and methods for identifying, in a network environment in which users watch videos that are downloaded or streamed over a network, a video in which a user is likely to be interested based on session context. For example, a server or other computing system identifies prior session contexts in which prior users watched videos and session progress data for prior sessions in which these prior users watched the videos. The server or other computing system determines a session context of a user for whom a video is to be recommended. For this user, the server or other computing system generates a recommendation identifying one or more videos in which the user is likely to be interested, where the user has not previously watched the recommended videos. The recommendation is generated based on the prior session contexts, the session progress data, and the session context of the user.