BETA: A Novel Learning-Based Adaptive Streaming Approach with Spatial and Temporal Optimization

Adaptive video streaming (DASH) has become a key technology in video transmission. Given the advantages of deep re-inforcement learning (DRL), streaming vendors are increasingly focusing on DRL-based adaptive bitrate (ABR) algorithms. In practice, almost all the ABR algorithms are designed with the in-tention to work well in any size/shape of networks. However, far from expectations, our measurements revealed that, even with extensive training on a vast scale of real network trace data, if the network condition varies over a wide range, the achieved Quality-of-Experience (QoE) is only 43.1% ~ 48.9%, far below the optimal 100%. We termed this problem as “ABR Under-Generalization”. To address this problem, we developed BETA in this work, a novel ABR-specialized DRL-based approach that integrates spatial and temporal modules. 1) Spatial: BETA offers a detector that identi-fies the potential network condition that may cause poor performance and then trains complementary ABR algorithms specifically functioning in it; 2) Temporal: At each training epoch, BETA learns from the decision experience of multiple-step segments to cover long-term environmental feedback. Extensive evaluations demonstrate that BETA achieves an average QoE improvement of 19.4% to 50.9% compared to the state-of-the-art ABR algorithms, with gains of up to 244.1% in the challenging networks with dramatically variable throughput.