CP-operated dash caching via reinforcement learning

In recent years, Dynamic Adaptive Streaming over HTTP (DASH) has gained momentum as an effective solution for delivering videos on the Internet. This trend is further driven by the deployment of existing HTTP cache infrastructures in DASH systems to reduce the traffic load as well as to serve clients better. However, deploying conventional cache servers in DASH systems still suffers from low cache hit ratio and bitrate oscillations, which makes it challenging for content providers (CPs) to balance the user-perceived quality-of-experience (QoE) and the operating cost in cache-enabled DASH systems. To address this challenge, we propose a CP-operated DASH caching framework to provide good user QoE with low cost. In particular, we first formulate the caching decision problem as a stochastic optimization problem over a finite time horizon. The objective of this problem is to maximize a weighted sum of the user QoE and the operating cost, termed as the utility. Then we design a reinforcement learning based online algorithm which can obtain approximately optimal solution of this problem. Through extensive trace-driven experiments, we show that our approach not only achieves 40% average improvement of the overall utility compared to baseline approaches, but also adapts to the server load.