What happens when HTTP adaptive streaming players compete for bandwidth?

By Saamer Akhshabi, Lakshmi Anantakrishnan, Ali C. Begen, C. Dovrolis. International Workshop on Network and Operating System Support for Digital Audio and Video, 2012

352 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/d4947c978ee17eff81358ae87ebe6594dbf04cb2

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Citations in the corpus

Developing a predictive model of quality of experience for internet video (2013, background): “…actionable and useful to guide the design of video delivery mechanisms; e.g., adaptive video player designers can use this model to tradeoff bitrate, join time, and buffering [12,13,21] and content providers can use it to evaluate cost-performance tradeoffs of different CDNs and bi-trates [1,28].”
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP (2015, background): “, [46, 17, 32, 34]) and several proposals have emerged to try and address these challenges (e.”
A Survey on Quality of Experience of HTTP Adaptive Streaming (2015, neutral): “This is in line with results described in [141] and [143] that report network underutilization as a result of video quality oscillation due to competing player instances.”
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”
A buffer-based approach to rate adaptation (2014, background): “Prior works have shown that sudden changes in available network capacity confuse existing ABR algorithms, causing the algorithms to either overestimate or underestimate the available network capacity [1, 2, 6, 10, 12].”
Confused, timid, and unstable: picking a video streaming rate is hard (2012, background): “However, [3] focuses on the unfairness problem among two video players, while in this work we focus on the unfairness problem between a video player and a longlived TCP flow.”
Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE (2012, background): “Our speciﬁc recommendations are (Section 3): (1) randomized chunk scheduling to avoid synchronization biases in sampling the network state, (2) a stateful bitrate selection that compensates for the biased interaction between bitrate and estimated bandwidth, (3) a delayed update approach to tradeoff…”