Adaptive Video Transcoding using Q-Learning for Real-Time Streaming Across Diverse Devices and Networks

As streaming is time-sensitive, efficient video transcoding is essential to real-time streaming; even more so when the user devices are heterogeneous and the network conditions are variable. Conventional transcoding techniques, based on fixed settings/transcoding rules may not provide the best viewing experience over a variety of devices and networks. These approaches cannot cope with the changing bandwidth, device limitations and content requirements. To overcome these challenges, we present a novel system which utilizes Q-learning, a reinforcement learning-based process, to continuously optimize video content according to the real-time device attributes of the user and network conditions. The model makes intelligent decisions regarding transcoding by including metadata derived in video platforms such as YouTube, including resolution, format and encoding type in addition to details about the user device and the network. The main performance indicators, which include transcoding latency, CPU/GPU load, format compatibility, etc., are enhanced to facilitate the delivery of video. This approach has shown good results when compared to the baseline models in terms of better and improved adaptability, efficiency, and performance.