An Edge Caching Strategy Based on User Speed and Content Popularity for Mobile Video Streaming

Mobile users’ demands to delay-sensitive video streaming media put forward new requirements for mobile networks, such as architecture optimization. Edge caching as a new paradigm is proposed to enhance the quality of service (QoS) for mobile users at the network edge. Due to the limited coverage of edge cache nodes, the frequent handoffs between base stations would aggravate network traffic overhead, resulting in a bad experience of high latency and service interruption when mobile users browse videos. This paper first proposes a three-layer mobile edge network architecture and applied edge caching to video streams to build an efficient caching system. Given the user’s mobility and low latency of mobile video streaming, we propose an edge caching strategy based on user speed and content popularity. Horizontally, the user’s speed affects the spanning area and the buffer size of the cache on edge; vertically, content popularity determines the priority of cached videos. Experimental results demonstrate that our caching strategy outperforms other schemes in terms of the average delay and the cache hit ratio in mobile video streaming scenes compared with the other three classic caching methods.

Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming

Video streaming application such as Youtube is one of the most popular mobile applications. To adjust the quality of video for available network bandwidth, a streaming server provides multiple representations of video of which bit rate has different bandwidth requirements. A streaming client utilizes an adaptive bit rate (ABR) scheme to select a proper representation that the network can support. However, in mobile environments, incorrect decisions of an ABR scheme often cause playback stalls that significantly degrade the quality of user experience, which can easily happen due to network dynamics. In this work, we propose a control theory (Linear Quadratic Optimization)-based ABR scheme to enhance the quality of experience in mobile video streaming. Our simulation study shows that our proposed ABR scheme successfully mitigates and shortens playback stalls while preserving the similar quality of streaming video compared to the state-of-the-art ABR schemes.