Video User Network Profiling and Its Application

1. Background

  1. Network Quality: Network quality ultimately influences the download rate of video streams. If the download rate is lower than the video bitrate for a long time or the jitter is severe, the playback may freeze easily. The download rate is influenced by a combination of various factors related to the playback process. We must evaluate the quality of each part of the process, so we can find out which part goes wrong once the download rate decreases. Then, we can apply a specific solution. This way, we can easily cope with any possible problems.
  2. Network Environment: Users are probably visiting home networks, public networks, or commuting. The environmental recognition here is no longer real-time data collection and analysis. It requires collecting data in advance. It requires understanding the use environments of users based on features, such as network traffic, to further predict possible playback events in this environment. Moreover, by applying custom policies, we can provide users with a better video viewing experience.

2. Network Profiling

  1. Data Cleansing: Raw data collection introduces dirty data due to problems, such as thread timing. The dirty data includes the value 0 or the maximum value. For example, the gateway delay Round-Trip Time (RTT) may be mixed with some data, such as -1, 0, or the timeout value. Abnormal values affect the final decision. Generally, we delete abnormal values. For missing values, we may delete or fill them. To fill such missing values, we can use the methods, including the mean completer, random filling, and k-nearest neighbor (KNN) filling, based on the condition.
  2. Data Standardization: The cleansed data is normalized, and the limit on units is removed to facilitate the comparison or weighting of different indicators. Finally, multiple features are converted into a multi-dimensional vector, and the vector is standardized to achieve data standardization. Both data with ms as a unit and values with kbps as a unit form an element in the vector.
  3. Feature Derivation and Selection: Feature derivation aims to convert the original features and calculate the required new data. For example, calculate the mean value, variance, standard deviation of features, and select X-quantile data to characterize features. For the gateway latency RTT, the RTT is collected multiple times, the mean or variance of the multiple acquisition results is calculated, and then the mean or variance is reported to represent the gateway latency RTT. For network card traffic, we expect to get its maximum value within a short time. Therefore, taking the mean value is not the best solution. Here, we take the 90th percentile value of data to characterize the data.
  1. The highest density parts are located in areas with low gateway latency.
  2. According to the horizontal axis, as the network speed increases, the possibility that the gateway latency has larger values decreases.

3. Application Scenarios

1) Weak Network Prompts for Users

2) Scheduling Optimization of Weak Networks

3) Download Optimization of Weak Networks

4) User Scenario Profiling

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