QDASH: a QoE-aware DASH system

By Ricky K. P. Mok, Xiapu Luo, Edmond W. W. Chan, Rocky K. C. Chang. ACM SIGMM Conference on Multimedia Systems, 2012

380 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/9750007ec312b8512a644d2be8923bfb984d5944

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In Computer Networks and Communications

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In Signal Processing

Citations in the corpus

A Survey on Quality of Experience of HTTP Adaptive Streaming (2015, neutral): “In [161], available bandwidth measurements are forwarded to the clients, whereas in [160], the proxy defines which quality level shall be used.”
Towards agile and smooth video adaptation in dynamic HTTP streaming (2012, background): “Recent study has shown that switching back-and-forth between different video versions will significantly degrade user video experience [7].”
Confused, timid, and unstable: picking a video streaming rate is hard (2012, background): “In [17], the authors implement a different buffer-aware rate selection algorithm and experimentally measure user preferences for gradual and infrequent playback rate changes.”
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].”
Learning in situ: a randomized experiment in video streaming (2019, background): “Researchers 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 controltheoretic schemes that try to ma”
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (2019, background): “QDASH consists of a QDASH-abw module to measure the bandwidth and a QDASH-qoe module that assists the client in choosing a suitable bitrate that can support the current network conditions and buffer occupancy.”
Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE (2012, methodology): “Other approaches use better bandwidth prediction and stability techniques (e.g., [34,38,40]).”
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP (2015, neutral): “, [31, 45, 42]), server-side software changes (e.”