| 1. |
- Ngai, Edith C.-H., et al.
(författare)
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Personalized Mobile-Assisted Smart Transportation
- 2016
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Ingår i: 2016 Digital Media Industry And Academic Forum (DMIAF). - 9781509010004 ; , s. 158-160
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Konferensbidrag (refereegranskat)abstract
- Digital media covers larger parts of our daily lives nowadays. Mobile services enable a better connected society where citizens can easily access public services, discover events, and obtain important information in the city. We observe the popularity of mobile car sharing applications, such as Uber and Didi Dache. Mobile social applications provide new ways of developing and optimizing public transportation. In this paper, we present a mobile platform for timetable-free traveling. It can capture the traffic demand of citizens in real-time, and support efficient planning and scheduling for vehicles on-demand. At the moment, the platform is targeted for public bus services, but it has great potential to be extended for self-driving vehicles in the future.
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| 2. |
- Zhang, Cong, et al.
(författare)
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Dependency-Aware Caching for HTTP Adaptive Streaming
- 2016
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Ingår i: 2016 Digital Media Industry And Academic Forum (DMIAF). - 9781509010004 ; , s. 89-93
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Konferensbidrag (refereegranskat)abstract
- There has been significant interest in the use of HTTP adaptive streaming for live or on-demand video over the Internet in recent years. To mitigate the streaming transmission delay and reduce the networking overhead, an effective and critical approach is to utilize cache servers between the origin servers and the heterogeneous clients. As the underlying protocol for web transactions, HTTP has great potentials to explore the resources within state-of-the-art CDNs tor caching; yet distinct challenges arise in the HTTP adaptive streaming context. After examining a long-term and large-scale adaptive streaming dataset as well as statistical analysis, we demonstrate that the switching requests among the different qualities frequently emerge and constitute a significant portion in a per-day view. Consequently, they have substantially affected the performance of cache servers and Quality-of-Experience (QoE) of viewers. In this paper, we propose a novel cache model that captures the dependency among the segments in the cache server for adaptive HTTP streaming. Our work does not assume any specific selection algorithm on the client's side and hence can be easily incorporated into existing streaming cache system. Its centralized nature is also well accommodated by the latest DASH specification. The performance evaluation shows our dependency-aware strategy can significantly improved the cache hit-ratio and QoE of HTTP streaming as compared to previous methods.
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