# Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
## 2 Related Work  
Several pieces of research have been done to address issues in video streaming under dynamic network conditions, such as incorrect bandwidth estimation, smoothness in streaming quality, and fairness across users.  
[PERSON] et al. [6] propose a feedback controller to drive the playback buffer length to a set-point, using adjustment factors to control bit rate according to network bandwidth and balance responsiveness and smoothness. While their controller utilizes the buffer status to determine the bit rate, the final decision strongly depends on bandwidth prediction.  
[PERSON] et al. [9] extend the approach in [6] to address large bandwidth variations and energy efficiency in cellular/WiFi networks. The proposed scheme chooses lower bit rates than an estimated throughput as a larger throughput variance becomes larger.  
[PERSON] et al. [10] also introduce a feedback control based on a Proportional-Integral (PI) controller using the playback buffer level as a feedback signal to the controller without adjusting control gains for optimal control.  
[PERSON] et al. [3] propose a bit rate adaptation mechanism based only on playback buffer status. The basic idea behind their approach is that buffer length increases if a requested video rate is lower than the available bandwidth, and decreases otherwise.  
[PERSON] et al. [5] focus on undesirable streaming behaviors, such as bit rate fluctuation, when multiple streaming clients compete for bandwidth. They show that these problems are because clients cannot perceive their fair-share bandwidth due to the discrete nature of the video bit rates. To resolve these issues, the authors introduce a proactive probing mechanism for estimating the target average video bit rate.  
[PERSON] et al. [7] define a streaming QoE optimization problem and propose a bit rate selection using the solutions. However, their approach requires pre-computing the required information for a specific video setting.  
Further, there are several approaches to utilize artificial intelligence (AI) algorithms for video streaming. [PERSON] et al. [11] propose the first ABR scheme that utilizes Deep Reinforcement Learning (DRL) to select bitrate for the next video chunk. [PERSON] et al. [12] suggest another RL-based ABR scheme. These approaches require training procedures to obtain an optimal DRL model in advance. Further, although RL can help a mobile client to choose the most proper bit rate according to network status, even inferences using a neural network requires large computation overhead with high energy consumption. This is inappropriate for mobile devices with low computing power and limited power supply; even though recent high-end mobile devices introduce an AI accelerator to support AI algorithms with low energy consumption, mid-range devices, which are popular in the market, still have a lack of such functionalities. [PERSON] et al. [13] suggest PERCEIVE using an LSTM (Long Short Term Memory) model to predict throughput according to cellular channel status. Similar to the previous approaches [11; 12], PERCEIVE can have concerns for computation overhead in mobile devices and it also targets WebRTC, not DASH video streaming.