TY - JOUR
T1 - Power control for wireless VBR video streaming
T2 - From optimization to reinforcement learning
AU - Ye, Chuang
AU - Gursoy, M. Cenk
AU - Velipasalar, Senem
N1 - Funding Information:
Manuscript received August 13, 2018; revised December 14, 2018 and February 23, 2019; accepted February 23, 2019. Date of publication March 25, 2019; date of current version August 14, 2019. This work was supported by the National Science Foundation under Grant CCF-1618615. The associate editor coordinating the review of this paper and approving it for publication was D. Niyato. (Corresponding author: M. Cenk Gursoy.) The authors are with the Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244 USA (e-mail: chye@syr.edu; mcgursoy@syr.edu; svelipas@syr.edu).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - Dynamic programming
KW - Playout buffer overflow
KW - Playout buffer underflow
KW - Power control
KW - Reinforcement learning
KW - Variable bit rate (VBR) video
KW - Video streaming
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U2 - 10.1109/TCOMM.2019.c
DO - 10.1109/TCOMM.2019.c
M3 - Article
AN - SCOPUS:85082393641
SN - 0090-6778
VL - 67
SP - 5629
EP - 5644
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 8
M1 - e2923412
ER -