A model for evaluating sharing policies for network-assisted HTTP adaptive streaming

Abstract

a b s t r a c t HTTP adaptive streaming (HAS) has become the dominant technology for streaming video over the Internet. It gained popularity because of its ability to adapt the video quality to the current network conditions and other appealing properties such as usage of off-the-shelf HTTP servers and easy firewall traversal. However, when multiple HAS players share a bottleneck link for streaming, the individual adaptation techniques in the players have difficulties to maintain a stable bitrate and fairly share the network resources. HAS-assisting network elements can solve these performance problems and allow execution of advanced policies for sharing the available bandwidth. Nonetheless, testing and evaluating new sharing policies is costly and time consuming. This motivated us to formulate a model that allows to differentiate between groups of users depending on the type of user or device, and that can describe the mean bitrate of the video streams and how often this bitrate is expected to change during playout. To show how our model can be used, we demonstrate two applications of our model. Furthermore, we validate the model based results against results obtained using our streaming testbed and proxy server based HASassistant. The results show that our model is highly accurate for both the mean bitrate and the number of changes in video bitrate. As such, our model is a useful tool for network administrators and internet service providers for evaluating the performance of sharing policies and for managing and provisioning video delivery networks.

Modelling of Content Adaptation in 5G Networks: A Survey

“…This model uses HTTP Adaptive Streaming (HAS) [5] where a video file is divided into segments between a time interval of two and ten seconds. Each of these segments is encoded at multiple resolutions and bitrates.…”

Using DASH Assisting Network Elements for Optimizing Video Streaming Quality

“…In extensive validations in [18,21], that compare our model with DASH performance using DANEs, we show that our model is highly accurate. Only when DASH players use large buffers, the model underestimates the expected streaming bitrate.…”

“…Only when DASH players use large buffers, the model underestimates the expected streaming bitrate. Since commercial players often use large buffer (at least 30 seconds), we improved our model to be sensitive to buffer sizes [21]. The model was applied in [20] to compare different sharing policies for DANEs: DASH priority, background traffic priority, and an in-between policy.…”

“…[16,18,20,22]) and journals (i.e [17,19,21]). A timeline overview of the contributions with corresponding papers is given in Figure 2.…”

Enhancing Over-the-Top Video Streaming Quality with DASH Assisting Network Elements

“…For example, policies for devices with different screen sizes, or for premium users, can be defined. An extended version of the model that includes sensitivity for buffer sizes is presented in [11]. Moreover, thorough evaluations are performed to validate the model and show its high accuracy.…”