A Fair Share for All: TCP-inspired Adaptation Logic for QoE Fairness among Heterogeneous HTTP Adaptive Video Streaming Clients

Michael Seufert, Nikolas Wehner, Pedro Casas-Hernandez

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung


This paper presents a novel adaptation logic for HTTP adaptive streaming (HAS), which achieves not only a high Quality of Experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known Transmission Control Protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this work, the design of the TCP-inspired adaptation logic (TCPAL) is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS adaptation logics. TCPAL is evaluated both in scenarios with stable and fluctuating streaming capacity, and the impact of its parameters is explored. The results suggest that TCPAL performs on par with other HAS adaptation logics in terms of QoE and QoE fairness for low link capacities, but significantly improves the QoE fairness for increased link capacity. Moreover, the fairness achieved by TCPAL does not degrade in situations with fluctuating streaming capacity.
FachzeitschriftIEEE Transactions on Network and Service Management
PublikationsstatusVeröffentlicht - 2019

Research Field

  • Ehemaliges Research Field - Experience Measurement
  • Ehemaliges Research Field - Data Science


  • Video streaming
  • HTTP adaptive streaming
  • Adaptation logic
  • Quality of Experience
  • Fairness
  • QoE fairness


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