Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE

By Junchen Jiang, Vyas Sekar, Hui Zhang. Conference on Emerging Network Experiment and Technology, 2012

1,017 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/8505c6184ab6b2b443f823150cf5a4248298937d

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Citations in the corpus

A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP (2015, neutral): “ISBN 978-1-4503-3542-3/15/08. . . $15.00 DOI: http://dx.doi.org/10.1145/2785956.2787486”
Oboe: auto-tuning video ABR algorithms to network conditions (2018, background): “Others have looked at quality issues that occur when multiple players start to compete for bandwidth [15, 30, 31, 34] In contrast, Oboe improves the QoE performance of several ABR”
Learning in situ: a randomized experiment in video streaming (2019, background): “…have produced a literature of ABR schemes, including “rate-based” approaches that focus on matching the video bitrate to the network throughput [20,23,27], “buffer-based” algorithms that steer the duration of the playback buffer [18,38,39], and control-theoretic schemes that try to maximize…”
Optimizing 360 video delivery over cellular networks (2016, methodology): “Extensive research has been conducted on improving the QoE of DASH video [18, 21, 13, 23, 17].”
From QoS to QoE: A Tutorial on Video Quality Assessment (2015, background): “Multi-user adaptive video streaming is considered in [182], [183].”
CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction (2016, methodology): “To meet these new application-level QoE goals, video players need intelligent bitrate selection and adaptation algorithms [27,30].”
A variegated look at 5G in the wild: performance, power, and QoE implications (2021, background): “For the following sections, we focus on mmWave 5G which are considered key to mainstream 5G and have not been studied before in the context of mobile applications. has proposed a plethora of ABR algorithms for video streaming in recent years [33, 38, 61, 62].”
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (2019, background): “[87] studied the limitations of video players when a large number of clients shared the same network by providing an experimental study that identified the main factors in bitrate selection.”
A buffer-based approach to rate adaptation (2014, background): “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].”
Neural Adaptive Video Streaming with Pensieve (2017, background): “For example, Festive [21] predicts throughput to be the harmonic mean of the experienced throughput for the past 5 chunk downloads.”
Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale (2013, methodology): “[14] defines inefficiency as | ∑ i ri,t−C| C for player i at time t.”
Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devices (2018, methodology): “In non-360° video streaming, numerous rate adaptation schemes have been proposed [34, 36, 44, 59].”