Energy Budget-Aware Video Quality Management in Transcoding Servers using Deep Reinforcement Learning under Dynamic Popularity Change

초록

Dynamic adaptive video streaming over HTTP (DASH), the de-facto standard in video streaming, requires significant CPU energy for transcoding. For carbon efficiency, it is essential to adhere to a low energy budget when using non-renewable energy sources. However, this can reduce the available bitrate versions, negatively impacting overall video quality. To tackle this trade-off, we propose a new deep reinforcement learning (DRL)-based scheme that limits energy consumption while enhancing video quality on transcoding servers. The scheme leverages a learning model that accounts for variable transcoding times and dynamic popularity changes, calculating the expected video quality, which is returned as a reward to the agent for each action when each bitrate version is transcoded. This allows the agent to decide on the transcoding of each bitrate version, ensuring the energy budget threshold is met while maximizing video quality. Experimental results show that the proposed scheme improves video quality between 2.7% and 18.3% (average, 10.9%) under various energy budgets.

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