Abstract
Adaptive bitrate (ABR) algorithms are routinely adopted for transmitting media contents across dynamic networks. State-of-the-art ABR algorithms only adapt to video bitrate without considering audio bitrate adaption as they consider the im-pact on the video to be negligible due to the small size of the audio. However, to bring users an immersive experience, more and more content providers have applied high-quality audio with large sizes, like stereophonic and surround (Dolby Atmos). Therefore, improper audio bitrate selection will ad-versely affect video bitrate selection, leading to undesirable audio/video combinations (the highest video quality with the lowest audio quality, vice versa) and frequent playback inter-ruptions. To address these inefficiencies, we propose a Self-Play reinforcement learning-based Audio-aware ABR algorithm named SPA to learn strategies for audio and video bi-trate selections. Experimental results demonstrate SPA's con-siderable superiority as compared with existing approaches.
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