Towards agile and smooth video adaptation in dynamic HTTP streaming

By Guibin Tian, Yong Liu. Conference on Emerging Network Experiment and Technology, 2012

302 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/50527a652a72616d55bab82c54d65b6479741098

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Citations in the corpus

Learning in situ: a randomized experiment in video streaming (2019, background): “Finally, Fugu’s predictor is a neural network, which lets it consider an array of diverse signals that relate to transmission time, including raw congestion-control statistics from the sender-side TCP implementation [17,42].”
Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale (2013, methodology): “Control-theoretical approaches, including use of a PID controller, are also considered by several works [6], [21], [22].”
Viewport-adaptive navigable 360-degree video delivery (2016, background): “Rate adaptation algorithms are developed [11, 20] to reduce the mismatch between the requested bit-rate and the throughput.”
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (2019, background): “Liu et al. [51] proposed a bitrate adaptation algorithm that tries to detect bandwidth fluctuations and congestion using a smoothed network throughput based on the segment fetch time (SFT), which measures the time starting from sending the HTTP GET request to receiving the last byte of the segment.”
A buffer-based approach to rate adaptation (2014, methodology): “Some leverage control theory to adjust the capacity estimation based on the buﬀer occupancy [5, 20], some smooth the quality degradation according to the buﬀer occupancy [15], and some randomize chunk scheduling depending on the buﬀer occupancy to have better samples of the channel [10].”
CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction (2016, background): “• Sessions sharing similar critical characteristics tend to exhibit similar throughput patterns.”
Oboe: auto-tuning video ABR algorithms to network conditions (2018, background): “ABR algorithms fall in two broad categories: (i) those that use both prediction of network throughput and buffer occupancy [34, 51, 59]; and (ii) those that are primarily based on buffer occupancy [32, 48].”