Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning

In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used...

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Vydané v:IEEE transactions on communications Ročník 67; číslo 8; s. 5629 - 5644
Hlavní autori: Ye, Chuang, Gursoy, M. Cenk, Velipasalar, Senem
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptive control
Algorithms
Bit rate
Buffer storage
Buffers
Completion time
Computer simulation
Control systems
Digital media
Dynamic programming
Energy consumption
Machine learning
Optimization
playout buffer overflow
playout buffer underflow
Policies
Power consumption
Power control
Receivers
reinforcement learning
Streaming media
variable bit rate (VBR) video
Video data
video streaming
Video transmission
Windows (intervals)
Wireless communication
ISSN:0090-6778, 1558-0857
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Shrnutí:In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used in dynamic adaptive streaming video applications. In this setting, we analyze power control policies considering the following two objectives: 1) the minimization of the transmit power consumption and 2) the minimization of the transmission completion time of the communication session. In order to play the video without interruptions, the power control policy should also satisfy the requirement in which the VBR video data is delivered to the mobile user without causing playout buffer underflow or overflows. A directional water-filling algorithm, which provides a simple and concise interpretation of the necessary optimality conditions, is identified as the optimal offline policy. Following this, two online policies are proposed for power control based on channel side information (CSI) prediction within a short time window. Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session. Subsequently, reinforcement learning (RL)-based approach is employed for the second online power control policy. Through the simulation results, we show that the optimal offline power control policy that minimizes the overall power consumption leads to substantial energy savings compared with the strategy of minimizing the time duration of video streaming. We also demonstrate that the RL algorithm performs better than the dynamic programming-based online grouped water-filling (GWF) strategy unless the channel is highly correlated.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2019.2907251