Abstract: In various embodiments, a simulation evaluation application generates a first streaming header based on rungs of a first candidate encoding ladder, where each rung specifies a resolution and a bitrate of a different encoded video. The simulation evaluation application executes an adaptive bitrate algorithm on the first streaming header based on a network throughput trace to determine a first value for a metric that is relevant to quality of experience. The simulation evaluation application generates a second streaming header based on a second candidate encoding ladder. The simulation evaluation application executes the adaptive bitrate algorithm on the second streaming header based on the network throughput trace to determine a second value for the first metric. The simulation evaluation application compares the first value to the second value to determine that the first candidate encoding ladder instead of the second candidate encoding ladder should be used to stream the media title.

Abstract: In various embodiments, an encoding ladder application generates encoding ladders that are used to stream media titles. The encoding ladder application generates an objective function based on a ladder configuration and a parameterized objective function. The parameterized objective function approximates a tradeoff between a quality of experience and a cost term associated with a candidate encoding ladder. The encoding ladder application generates constraints based on the ladder configuration and parameterized constraints. The encoding ladder application executes a constrained optimization algorithm on the objective function, the constraints, and encoding point metadata associated with a set of encoded videos to generate a first candidate encoding ladder for a media title.

Abstract: In various embodiments, a media delivery application transmits encoded chunks of a media title to a playback application. In operation, the media delivery application receives, via a media channel, an encoded chunk request that has been transmitted over a TCP connection. The media delivery application also receives, via a side channel, a pacing specification that is associated with the encoded chunk request and has been transmitted over the TCP connection. As per the encoded chunk request, the media delivery application retrieves encoded chunk content. The media delivery application sets a parameter associated with the TCP connection equal to a parameter value based on the pacing specification. Subsequently, the media delivery application causes TCP segments corresponding to the encoded chunk content to be transmitted, via the media channel, over the TCP connection in accordance with the first parameter value.

Abstract: In various embodiments, a hindsight application computes a hindsight metric value for evaluation of a video rate selection algorithm. The hindsight application determines a first encoding option associated with a source chunk of a media title based on a network throughput trace and a buffer trellis. The hindsight application determines that the first encoding option is associated with a buffered duration range. The buffered duration range is also associated with a second encoding option that is stored in the buffer trellis. After determining that the first encoding option is associated with a higher visual quality than the second encoding option, the hindsight application stores the first encoding option instead of the second encoding option in the buffer trellis to generate a modified buffer trellis. Finally, the hindsight application computes a hindsight metric value associated with a sequence of encoded chunks of the media title based on the modified buffer trellis.

Abstract: In various embodiments, a media delivery application transmits encoded chunks of a media title to a playback application. In operation, the media delivery application receives, via a media channel, an encoded chunk request that has been transmitted over a TCP connection. The media delivery application also receives, via a side channel, a pacing specification that is associated with the encoded chunk request and has been transmitted over the TCP connection. As per the encoded chunk request, the media delivery application retrieves encoded chunk content. The media delivery application sets a parameter associated with the TCP connection equal to a parameter value based on the pacing specification. Subsequently, the media delivery application causes TCP segments corresponding to the encoded chunk content to be transmitted, via the media channel, over the TCP connection in accordance with the first parameter value.

Abstract: In various embodiments, a hindsight application computes a total download size for a sequence of encoded chunks associated with a media title for evaluation of at least one aspect of a video streaming service. The hindsight application computes a feasible download end time associated with a source chunk of the media title based on a network throughput trace and a subsequent feasible download end time associated with a subsequent source chunk of the media title. The hindsight application then selects an encoded chunk associated with the source chunk based on the network throughput trace, the feasible download end time, and a preceding download end time associated with a preceding source chunk of the media title. Subsequently, the hindsight application computes the total download size based on the number of encoded bits included in the first encoded chunk. The total download size correlates to an upper bound on visual quality.

Abstract: In various embodiments, a hindsight application computes a hindsight metric value for evaluation of a video rate selection algorithm. The hindsight application determines a first encoding option associated with a source chunk of a media title based on a network throughput trace and a buffer trellis. The hindsight application determines that the first encoding option is associated with a buffered duration range. The buffered duration range is also associated with a second encoding option that is stored in the buffer trellis. After determining that the first encoding option is associated with a higher visual quality than the second encoding option, the hindsight application stores the first encoding option instead of the second encoding option in the buffer trellis to generate a modified buffer trellis. Finally, the hindsight application computes a hindsight metric value associated with a sequence of encoded chunks of the media title based on the modified buffer trellis.

Abstract: In various embodiments, a hindsight application computes a total download size for a sequence of encoded chunks associated with a media title for evaluation of at least one aspect of a video streaming service. The hindsight application computes a feasible download end time associated with a source chunk of the media title based on a network throughput trace and a subsequent feasible download end time associated with a subsequent source chunk of the media title. The hindsight application then selects an encoded chunk associated with the source chunk based on the network throughput trace, the feasible download end time, and a preceding download end time associated with a preceding source chunk of the media title. Subsequently, the hindsight application computes the total download size based on the number of encoded bits included in the first encoded chunk. The total download size correlates to an upper bound on visual quality.

Abstract: In various embodiments, a hindsight application computes a hindsight metric value for evaluation of a video rate selection algorithm. The hindsight application determines a first encoding option associated with a source chunk of a media title based on a network throughput trace and a buffer trellis. The hindsight application determines that the first encoding option is associated with a buffered duration range. The buffered duration range is also associated with a second encoding option that is stored in the buffer trellis. After determining that the first encoding option is associated with a higher visual quality than the second encoding option, the hindsight application stores the first encoding option instead of the second encoding option in the buffer trellis to generate a modified buffer trellis. Finally, the hindsight application computes a hindsight metric value associated with a sequence of encoded chunks of the media title based on the modified buffer trellis.

Abstract: In various embodiments, a hindsight application computes a total download size for a sequence of encoded chunks associated with a media title for evaluation of at least one aspect of a video streaming service. The hindsight application computes a feasible download end time associated with a source chunk of the media title based on a network throughput trace and a subsequent feasible download end time associated with a subsequent source chunk of the media title. The hindsight application then selects an encoded chunk associated with the source chunk based on the network throughput trace, the feasible download end time, and a preceding download end time associated with a preceding source chunk of the media title. Subsequently, the hindsight application computes the total download size based on the number of encoded bits included in the first encoded chunk. The total download size correlates to an upper bound on visual quality.

Abstract: In various embodiments, a hindsight application computes a hindsight metric value for evaluation of a video rate selection algorithm. The hindsight application determines a first encoding option associated with a source chunk of a media title based on a network throughput trace and a buffer trellis. The hindsight application determines that the first encoding option is associated with a buffered duration range. The buffered duration range is also associated with a second encoding option that is stored in the buffer trellis. After determining that the first encoding option is associated with a higher visual quality than the second encoding option, the hindsight application stores the first encoding option instead of the second encoding option in the buffer trellis to generate a modified buffer trellis. Finally, the hindsight application computes a hindsight metric value associated with a sequence of encoded chunks of the media title based on the modified buffer trellis.

Abstract: In various embodiments, a hindsight application computes a total download size for a sequence of encoded chunks associated with a media title for evaluation of at least one aspect of a video streaming service. The hindsight application computes a feasible download end time associated with a source chunk of the media title based on a network throughput trace and a subsequent feasible download end time associated with a subsequent source chunk of the media title. The hindsight application then selects an encoded chunk associated with the source chunk based on the network throughput trace, the feasible download end time, and a preceding download end time associated with a preceding source chunk of the media title. Subsequently, the hindsight application computes the total download size based on the number of encoded bits included in the first encoded chunk. The total download size correlates to an upper bound on visual quality.

Abstract: A frequency hopping method for localization system is aimed to overcome the degradation of location accuracy due to radio interference if there are some other radio devices using the same radio frequency as a localization system. A Packet Reception Rate (PRR) thresholding or a learning-based approach for the diagnostic test is proposed. In that, a PRR thresholding or a set of parameters trained by Hidden Markov Model (HMM) is used as a criterion to decide whether or not to hop. The proposed hopping mechanism provides an accurate and stable localization with a minimum delay.

Abstract: A frequency hopping method for localization system is aimed to overcome the degradation of location accuracy due to radio interference if there are some other radio devices using the same radio frequency as a localization system. A Packet Reception Rate (PRR) thresholding or a learning-based approach for the diagnostic test is proposed. In that, a PRR thresholding or a set of parameters trained by Hidden Markov Model (HMM) is used as a criterion to decide whether or not to hop. The proposed hopping mechanism provides an accurate and stable localization with a minimum delay.