Reinforcement Learning based HTTP Adaptive Streaming Client

  • Van Leekwijck W.
  • Wu T.

At present, HTTP Adaptive Streaming (HAS) is developing into a key technology for video delivery over the Internet. In this delivery strategy, the client proactively requests a quality version of chunked video segments based on its perceived network bandwidth and other relevant factors. However, it is often observed that current HAS client heuristics lack flexibility in vast variable network environments. In this paper, a reinforcement-learning based HAS client is proposed to progressively learn the optimal request strategy by continuously maximizing a pre-defined reward function and to take over the decision process of heuristics. We study several components of the reinforcement learning, including selection of environment variables and definition of Quality of Experience (QoE)-related reward function, and implement it into the HAS client. Experimental results show that comparing with a conventional HAS system, the employed technique is more robust and flexible in the versatile network conditions, resulting in an enhanced QoE at the HAS client.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...