Title: Towards Wi-Fi AP-Assisted Content Prefetching for On-Demand TV Series: A Reinforcement Learning Approach,
Abstract: The emergence of smart Wi-Fi APs (Access Point), which are equipped with huge
storage space, opens a new research area on how to utilize these resources at
the edge network to improve users' quality of experience (QoE) (e.g., a short
startup delay and smooth playback). One important research interest in this
area is content prefetching, which predicts and accurately fetches contents
ahead of users' requests to shift the traffic away during peak periods.
However, in practice, the different video watching patterns among users, and
the varying network connection status lead to the time-varying server load,
which eventually makes the content prefetching problem challenging. To
understand this challenge, this paper first performs a large-scale measurement
study on users' AP connection and TV series watching patterns using
real-traces. Then, based on the obtained insights, we formulate the content
prefetching problem as a Markov Decision Process (MDP). The objective is to
strike a balance between the increased prefetching&storage cost incurred by
incorrect prediction and the reduced content download delay because of
successful prediction. A learning-based approach is proposed to solve this
problem and another three algorithms are adopted as baselines. In particular,
first, we investigate the performance lower bound by using a random algorithm,
and the upper bound by using an ideal offline approach. Then, we present a
heuristic algorithm as another baseline. Finally, we design a reinforcement
learning algorithm that is more practical to work in the online manner. Through
extensive trace-based experiments, we demonstrate the performance gain of our
design. Remarkably, our learning-based algorithm achieves a better precision
and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to
the random (resp. heuristic) algorithm.