Network-layer fairness for adaptive video streams

Network-layer fairness for adaptive video streams Recent studies observe that competing adaptive video streaming applications generate flows that lead to instability, under-utilization, and unfairness in bottleneck link sharing within the network. Additional measurements suggest there may also be a negative impact on users’ perceived quality of service as a consequence. While it may be intuitive to resolve application-generated issues at the application layer, in this paper we explore the merits of a network layer solution. We are motivated by the observation that traditional network-layer metrics associated with throughput, loss, and delay are inadequate to the task. To bridge this gap we present a network-layer QoS framework for adaptive streaming video fairness that reflect the video user’s quality of experience (QoE). We begin first by deriving a new measure to describe user-level fairness among competing flows, one that reflects the dynamics between the videoencoding and its mapping to a screen with a given size and resolution. We then design and implement our framework in VHS (Video-Home-Shaper) to evaluate performance in the home’s last access hop where this problem is known to exist. Experiments using a variety of devices, O/S platforms, and viewing screens demonstrate the merits of using video QoE as a basis for fair bandwidth sharing.