The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP
Open accessAbstract
User-perceived quality-of-experience (QoE) is critical in Internet video applications as it impacts revenues for content providers and delivery systems. Given that there is little support in the network for optimizing such measures, bottlenecks could occur anywhere in the delivery system. Consequently, a robust bitrate adaptation algorithm in client-side players is critical to ensure good user experience. Previous studies have shown key limitations of state-of-art commercial solutions and proposed a range of heuristic fixes. Despite the emergence of several proposals, there is still a distinct lack of consensus on: (1) How best to design this client-side bitrate adaptation logic (e.g., use rate estimates vs. buffer occupancy); (2) How well specific classes of approaches will perform under diverse operating regimes (e.g., high throughput variability); or (3) How do they actually balance different QoE objectives (e.g., startup delay vs. rebuffering). To this end, this paper makes three key technical contributions. First, to bring some rigor to this space, we develop a principled control-theoretic model to reason about a broad spectrum of strategies. Second, we propose a novel model predictive control algorithm that can optimally combine throughput and buffer occupancy information to outperform traditional approaches. Third, we present a practical implementation in a reference video player to validate our approach using realistic trace-driven emulations.
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Cites
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Self-citationUnderstanding the impact of video quality on user engagement
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Self-citationProbe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale
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Resource Management with Deep Reinforcement Learning
Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016) · Proceedings of the 15th ACM Workshop on Hot Topics in Networks · doi:10.1145/3005745.3005750 · Cited by 1,353
DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
Ran, X., Chen, H., Zhu, X., Liu, Z., & Chen, J. (2018) · IEEE INFOCOM 2018 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2018.8485905 · Cited by 450
CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction
Sun, Y., Yin, X., Jiang, J., Sekar, V., Lin, F., Wang, N., Liu, T., & Sinopoli, B. (2016) · Proceedings of the 2016 ACM SIGCOMM Conference · doi:10.1145/2934872.2934898 · Cited by 407
Self-citationOptimizing 360 video delivery over cellular networks
Qian, F., Ji, L., Han, B., & Gopalakrishnan, V. (2016) · Proceedings of the 5th Workshop on All Things Cellular: Operations, Applications and Challenges · doi:10.1145/2980055.2980056 · Cited by 402
Cellular Network Traffic Scheduling With Deep Reinforcement Learning
Chinchali, S., Hu, P., Chu, T., Sharma, M., Bansal, M., Misra, R., Pavone, M., & Katti, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11339 · Cited by 218
“Hence, we turn to stochastic Model Predictive Control (MPC) since it is a natural competitor to RL and has been deployed with widespread success in domains ranging from industrial process control (Camacho and Alba 2013) to cellular networking (Yin et al. 2015).”
Method
Metadata
- Authors
- Yin, X., Jindal, A., Sekar, V., & Sinopoli, B.
- Venue
- Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication
- Publisher
- ACM
- Published
- 2015-08-17
- Pages
- 325-338
- OpenAlex
- W1976944900
- Cited by
- 1,016
- References
- 35
- FWCI
- 29.51
- Type
- conference paper
- Language
- en
- Open access
- gold
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