With the growing popularity and prosperity of living streaming applications, it is naturally confronting users' quality of experience (QoE) degradation issues especially under dynamic environments arised from nonnegligible factors such as high latency and intermittent bitrate. In this paper, we propose an efficient adaptive bitrate (ABR) algorithm called BitLat to achieve both bitrate-control and latency-control. BitLat is based on reinforcement learning to get strong adaptability for dealing with the complex and changing network conditions. More specifically, in our work, we determine the specific value of latency threshold with the help of current advanced algorithm, and design the structure of the neural network in reinforcement learning, the features used in the training process, and the corresponding reward function. Additional, we use the Dynamic Reward Method to further enhance the performance. Comprehensive experiments are conducted to demonstrate BitLat outperforms the state-of-the-art ABR algorithms, with improvements in average QoE of 20%-62%.
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BitLat: Bitrate-adaptivity and Latency-awareness Algorithm for Live Video Streaming
Semantic Scholar · Computer Science · 2019
Abstract
With the growing popularity and prosperity of living streaming applications, it is naturally confronting users' quality of experience (QoE) degradation issues especially under dynamic environments arised from nonnegligible factors such as high latency and intermittent bitrate. In this paper, we propose an efficient adaptive bitrate (ABR) algorithm called BitLat to achieve both bitrate-control and latency-control. BitLat is based on reinforcement learning to get strong adaptability for dealing with the complex and changing network conditions. More specifically, in our work, we determine the specific value of latency threshold with the help of current advanced algorithm, and design the structure of the neural network in reinforcement learning, the features used in the training process, and the corresponding reward function. Additional, we use the Dynamic Reward Method to further enhance the performance. Comprehensive experiments are conducted to demonstrate BitLat outperforms the state-of-the-art ABR algorithms, with improvements in average QoE of 20%-62%.