Proceedings ArticleDOI
A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP
Xiaoqi Yin,Abhishek Jindal,Vyas Sekar,Bruno Sinopoli +3 more
- Vol. 45, Iss: 4, pp 325-338
TLDR
A principled control-theoretic model is developed that can optimally combine throughput and buffer occupancy information to outperform traditional approaches in bitrate adaptation in client-side players and is presented as a novel model predictive control algorithm.Abstract:
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.read more
Citations
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Journal ArticleDOI
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nguyen Cong Luong,Dinh Thai Hoang,Shimin Gong,Dusit Niyato,Ping Wang,Ying-Chang Liang,Dong In Kim +6 more
TL;DR: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection.
Proceedings ArticleDOI
Resource Management with Deep Reinforcement Learning
TL;DR: This work presents DeepRM, an example solution that translates the problem of packing tasks with multiple resource demands into a learning problem, and shows that it performs comparably to state-of-the-art heuristics, adapts to different conditions, converges quickly, and learns strategies that are sensible in hindsight.
Proceedings ArticleDOI
Neural Adaptive Video Streaming with Pensieve
TL;DR: P Pensieve is proposed, a system that generates ABR algorithms using reinforcement learning (RL), and outperforms the best state-of-the-art scheme, with improvements in average QoE of 12%--25%.
Journal ArticleDOI
Digital control of dynamic systems
TL;DR: Digital Control Of Dynamic Systems This well-respected, market-leading text discusses the use of digital computers in the real-time control of dynamic systems with an emphasis on the design of digital controls that achieve good dynamic response and small errors while using signals that are sampled in time and quantized in amplitude.
Proceedings ArticleDOI
BOLA: Near-optimal bitrate adaptation for online videos
TL;DR: This work formulate bitrate adaptation as a utility maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization techniques to minimize rebuffering and maximize video quality and proves that B OLA achieves a time-average utility that is within an additive term O(1/V) of the optimal value.
References
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Book
Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
Book
Model Predictive Control
TL;DR: In this article, the authors present a model predictive controller for a water heating system, which is based on the T Polynomial Process (TOP) model of the MPC.
Book
Digital control of dynamic systems
TL;DR: This well-respected, market-leading text discusses the use of digital computers in the real-time control of dynamic systems and thoroughly integrates MATLAB statements and problems to offer readers a complete design picture.
Journal ArticleDOI
An integrated experimental environment for distributed systems and networks
Brian S. White,Jay Lepreau,Leigh Stoller,Robert Ricci,Shashi Guruprasad,Mac Newbold,Mike Hibler,Chad Barb,Abhijeet Joglekar +8 more
TL;DR: The overall design and implementation of Netbed is presented and its ability to improve experimental automation and efficiency is demonstrated, leading to new methods of experimentation, including automated parameter-space studies within emulation and straightforward comparisons of simulated, emulated, and wide-area scenarios.
Journal ArticleDOI
Fast Model Predictive Control Using Online Optimization
Yang Wang,Stephen Boyd +1 more
TL;DR: A collection of methods for improving the speed of MPC, using online optimization, which can compute the control action on the order of 100 times faster than a method that uses a generic optimizer.