Abstract
Dynamic Adaptive Streaming over HTTP (DASH) is a promising scheme for improving the Quality of Experience (QoE) of users in video streaming. However, the existing schemes do not perform coordination among clients and depend on fixed heuristics. In this paper, we propose an adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme improves the overall QoE of clients and QoE fairness among clients based on a state-of-the-art reinforcement learning algorithm. Edge computing assistance plays a role in providing client-side observations to the mobile edge, making agents utilize this information when generating a policy for multi-client adaptive streaming. We evaluated the proposed scheme through simulation-based experiments under various network conditions. The experimental results show that the proposed scheme achieves better performance than the existing schemes.
Highlights
Using the existing HTTP infrastructure, Dynamic Adaptive Streaming over HTTP (DASH) adapts the bitrate of video segments delivered over the network to improve resource utilization and the Quality of Experience (QoE) for users [3–5]
We utilize edge computing and reinforcement learning to improve the performance of multiclient adaptive streaming
The proposed scheme aims to optimize the performance of multi-client adaptive streaming
Summary
According to the Cisco Annual Internet Report (2018–2023), the total number of global mobile subscribers will increase from 66% of the population in 2018 to 71% of the population by 2023 [1]. Video services such as Netflix and YouTube contribute to the major portion of Internet traffic. Using the existing HTTP infrastructure, DASH adapts the bitrate of video segments delivered over the network to improve resource utilization and the QoE for users [3–5]. Various studies based on DASH have been conducted over several years [6–10] These schemes perform bitrate adaptation by determining the video bitrate fit to the measured available bandwidth, current buffer level, or other predicted conditions
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