QoE Maximization for Video Streaming in Cache-Enable Satellite-UAV-Terrestrial Network

Unmanned aerial vehicle (UAV)-assisted video streaming is gaining growing interests in satellite-terrestrial networks due to the mobility and caching capability. However, it is challenging to perform trajectory planning and cache management towards maximizing quality of experience (QoE) for video streaming due to a dynamic network topology and a class of hybrid control actions. In this paper, we consider a QoE-oriented video streaming transport system in satellite-UAV-terrestrial network. Our goal is to design a transmission scheduling policy that can maximize the QoE received by the ground users (GUs) under the cache capacity constraints. In this regard, we formulate a scheduling problem as a cache-constrained Markov decision process (CMDP). To tackle the CMDP, we propose a novel hybrid reinforcement learning algorithm with risk sensibility. Extensive simulations show that our proposed scheme improves QoE by more than 50% over the conventionally configured schemes.

I. Introduction

With mobile video streaming gradually dominating global data traffic, Cisco's report shows that more than 66% of the content on installed flat panel TVs is 4K video, which is a challenge to satisfy the high quality of experience (QoE) requirements of remote cloud servers [1]. To mitigate this problem, edge caching has been ubiquitous by caching video streaming at a variety of edge devices which can directly transmit the video streaming to the ground users (GUs) to reduce latency. However, edge devices with limited coverage area are always deployed at fixed terrestrial nodes which makes them inefficient especially in hotspot areas.