Hulu, LLC

Beijing Yojaja Software Technology Development Co., Ltd.

Beijing Hulu Software Technology Development Co., Ltd.

Tongyu Dai

Wenhao Zhang

Deliang Fu

Chao Li

Lan Xie

Yuting Gui

Si Chen

Qiang She

Lemei Huang

Yicheng Liu

Xizhi Xu

Yanping Zhou

Chenyu Tian

Boya Lai

Weiran Shi

Renfei Shang

In some embodiments, a method receives quality metric values for profile levels in a plurality of profile levels for a segment of content. An available bandwidth and associated bitrates of profile levels in the plurality of profile levels are evaluated to select a first profile level. The quality metric values for profile levels in the plurality of profile levels are evaluated to select a second profile level. The method selects a profile level in the plurality of profile levels based on the first profile level and the second profile level. The profile level that is selected is requested for the segment.

Adaptive bitrate streaming using video quality information

In some embodiments, a method receives a network trace from a playback session of a delivery of content being managed by a content management system. The network trace is decomposed into a first sub-trace and a second sub-trace based on a threshold. The threshold is used to divide the network trace into the first sub-trace and the second sub-trace. A value is extracted for a feature for the network trace based on the first sub-trace. The method classifies the network trace into a category based on the value and performs an action based on the category for the content management system.

Network trace categorization in content delivery

In some embodiments, a method determines training data from first nearline features for a first sliding time window and a fixed time window. The first sliding time window changes during a first time period and the fixed time window is static during the first time period. A prediction model is trained using the training data. The prediction model is trained for a second time period in the first time period. The method determines values for second nearline features for a second sliding time window and a fixed time window for a request for a current session that is received during the second time period. The second sliding time window is based on a time for the request. The values are input for the second nearline features into the prediction model to generate a prediction. The method performs an action for the current session based on the prediction.

Content playback optimization using nearline information

In some embodiments, a method generates a reward function for an adaptive bitrate function that is based on a bandwidth and profiles in a profile ladder for a current segment of content. The reward function indicates a first profile should be selected for the current segment. The method performs a quality change analysis to select a second profile from the profile. The quality change analysis analyzes a quality change that is based on a previously selected profile from a prior segment and profiles in the profile ladder. The second profile is requested from the profile ladder for the current segment.

Cross-period quality smoothing in adaptive bitrate algorithm

In some embodiments, a method sends a set of first requests for a set of first segments of a video in a playback session. A first protocol from a plurality of protocols is specified in at least one of the set of first requests. The set of first segments is received. The method determines whether to switch from using the first protocol to a second protocol in the playback session based on receiving the set of first segments. When switching to the second protocol, the method sends a second request in the playback session, wherein the second request indicates the second protocol is to be used to send a second segment of the video.

Multiple protocol prediction and in-session adaptation in video streaming

In some embodiments, a method receives a request for an instance of media content in a current session. A state is retrieved for content delivery networks that is determined for a session in a set of sessions. The state includes a first set of values for a performance metric, and the first set of values is associated with the content delivery networks. The method determines a second set of values for the performance metric for the current session. The second set of values is associated with the content delivery networks. A content delivery network is selected for the request in the current session based on treating the current session as a session in the set of sessions. The session for the current session is after the session for the state in the set of sessions.

Content delivery network (CDN) selection using performance metric

In some embodiments, after receiving a first set of video segments of a video, a method starts a first computing instance and receives a video segment of the video using the first computing instance. A measurement is calculated based on receiving the video segment. The method uses the measurement to determine whether the first computing instance should continue to be used to receive video segments in the video or should be terminated to not receive video segments in the video.

Concurrent downloading of video

In some embodiments, a method determines an input value for a request for content and retrieves a plurality of states for a plurality of content delivery networks. The state includes a component that is based on variance. A plurality of scores for a plurality of content delivery networks is generated based on the input value and the plurality of states. A score for a content delivery network is generated using a parameter value for the component and the input value. The method selects a content delivery network from the plurality of content delivery networks based on the score for the content delivery network and outputs information for the selected content delivery network to cause the selected content delivery network to service the request to deliver the content.

Selection of content delivery networks using agents

In some embodiments, a method sends a set of requests for a video using a first protocol. The first protocol is selected from a plurality of protocols available for use in a playback session for the video. The method compares a first performance of the first protocol and a second performance of a second protocol and determines whether to switch from using the first protocol to the second protocol in the playback session based on the comparing.

In some embodiments, a method receives a request for a video and receives a ranking of content delivery networks for the request. Also, global constraints are received for the content delivery networks. The global constraints define an allocation for respective content delivery networks. The method selects a content delivery network using the global constraints and the ranking. A score for the content delivery network is altered from a first score to a second score in the ranking and used in the selecting of the content delivery network. The content delivery network that is selected for the request is outputted.

Global constraint-based content delivery network (CDN) selection in a video streaming system

In some embodiments, a method receives session features for a session associated with a request for a video from a client and predicts network conditions for the session using the session features. A subset of available profiles is selected based on the network conditions. The available profiles are associated with a different playback characteristic. The method provides a profile ladder that includes the subset of available profiles for the playback of the video to the client. The profile ladder restricts the client to using the subset of available profiles to request segments of the video during the session.

Session based adaptive playback profile decision for video streaming

In some embodiments, during playback of a video, using a parameter value of an adaptive bitrate algorithm to analyze playback of the video. The adaptive bitrate algorithm uses the parameter value to select a profile from a plurality of profiles to use to request segments of the video. A method selects a scenario in a plurality of scenarios when a playback condition meets a characteristic value of the scenario. The plurality of scenarios are associated with a different characteristic value. The method adjusts the parameter value of the adaptive bitrate algorithm based on the scenario to generate an adjusted parameter value. The plurality of scenarios have at least one different parameter value. The adjusted parameter value of the adaptive bitrate algorithm is used to analyze the playback of the video.

Dyanamic parameter adjustment for adaptive bitrate algorithm

In some embodiments, a method receives a plurality of video segments of a video using a plurality of computing instances and measures data received for the plurality of computing instances during a time window. A video segment for a computing instance in the plurality of computing instances is not fully downloaded during the time window. The method calculates a video delivery metric for the plurality of computing instances based on the data received and calculates an evaluation measurement based on the video delivery metric. The evaluation measurement is used to control an addition or a termination of a computing instance.

In some embodiments, a method receives a first ranking of content delivery networks for a playback session of a video and receives global constraints for the content delivery networks. The global constraints define an allocation for respective content delivery networks. The method alters a score for a content delivery network in the first ranking to generate a second ranking of content delivery networks. Then, a content delivery network is selected from the second ranking for the playback session of the video.

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Tongyu Dai