The exponential growth of mobile data and video traffic has risen to many new challenges for the research community in recent years. 5G/future networks are expected to support high throughput, minimal end-to-end delay, and ultra-high-quality delivery for various purposes such as mobile broadcasting, remote surgery, and augmented reality.

Two of the main expectations of 5G networks are that they will handle ultra-high-definition (UHD) video streaming and deliver services that meet the requirements of the end user’s perceived quality by adopting quality of experience (QoE) aware network management approaches.

QoS defined as key network performance metrics; however, QoE referred to an actual individual user experience. Many questions need to be addressed for QoS to QoE mapping, for instance.

  • Which are key patterns in QoS metrics that impact users’ QoE?
  • Does the expected QoE hold under varying network conditions?
  • During a video session, video streaming freezed often or even stopped, resulting in negative feedback from users, but which Key Performance Indicators (KPIs) caused the quality degradation?

To answer the above questions and many more, we present the Dynamic Adaptive Streaming over HTTP (DASH) Quality of Experience framework featuring a set of tools and dependencies to deliver realistic 5G network scenarios to observe QoS-to-QoE behaviours.

We present a reproducible data-driven framework to analyze state-of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key network QoS parameters by replaying static and mobility 5G traces.

The workflow of the proposed framework is summarized in the figure below.

dash qoe framework

Tools installed in DASH QoE Framework

Godash Player

Godash is implemented in “golang” and plays HAS video content without actually decoding actual video to consume less resources.  The player supports various state-of-art ABS algorithms that provide a complete functionality to stream current HTTP adaptive streaming with two protocols: TCP and QUIC. An example output “godash” player is listed in Table 1.

Seg_#Arr_TimeDel_TimeStallRep_LevelDel_RateAct_RateByte_SizeBuff_LevelAlgorithmP.1203
159580239294831322000Elastic1.878
2134357202391153329824554000Elastic1.878
333761030037813997201801643968Elastic1.90
45273967037814537031758144071Elastic1.903
57101867057214406241560674243Elastic2.038

DASH QoE Framework

Next, we fetch per-segment QoS features from the Pcaps collected during each experiment and merged them with “godash” logs, as shown in Table below.

Seg_#Arr_TimeDel_TimeStallRep_LevelDel_RateAct_RateByte_SizeBuff_LevelAlgorithmP.1203RTTThroughputPackets
159580239294831322000Elastic1.8780.0977587096.7742
2134357202391153329824554000Elastic1.87810.176161488.317230
333761030037813997201801643968Elastic1.9030.358184478.549864
45273967037814537031758144071Elastic1.90311.712192081.99362
57101867057214406241560674243Elastic2.03811.801191207.091955

Download the Framework

The framework includes a pre-configured VM (download)  with all software and dependencies installed.

Video instructions to use the framework is also available inside the VM.

Conclusions

We have released a flexible DASH QoE evaluation framework to run realistic investigations of QoS impact on QoE.

If you happen to use the DASH framework in your research, please reference the following publications:

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