Video User Network Profiling and Its Application

1. Background

Video apps are the main consumers of mobile network traffic. Compared with the user experience of other apps, video apps depend more on network environments. The core video experience indicators, such as the success rate, stalling rate, and the HD proportion of video playback, are related to product performance in network environments.

  1. 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

To evaluate the network during playback, a common method is to estimate the download speed of video fragments or the rate at which the player buffer drops. To be sure, the download speed and the buffer drop rate can reflect the end-to-end performance of the playback process. In engineering practices, we expect to grasp information on more dimensions to adopt different playback policies. For example, we can switch the playback link to the standby Content Delivery Network (CDN) if a fault at the CDN side leads to a sudden decrease in the download speed of video fragments. If a user’s Local Area Network (LAN) bandwidth is congested, we can play the video stream with a lower bitrate by switching the smart gear. After we perceive the network changes during video playback and analyze the causes of download speed changes, we can take appropriate measures to improve the playback experience.

  1. 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.
  2. 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. According to the horizontal axis, as the network speed increases, the possibility that the gateway latency has larger values decreases.

3. Application Scenarios

Network quality analysis provides multi-dimensional results, which accurately explain the causes of network faults. For different types of problems, the application of corresponding strategies can achieve the desired results. Table 1 lists the policies for different types of weak networks.

1) Weak Network Prompts for Users

In the case of a weak network buffer, if the signal latency or LAN latency is high, as shown in Figure 4, users will be reminded on the buffer page to guide themselves to perform related optimization. The stutter measurement result of the customer service system also gives corresponding prompts.

2) Scheduling Optimization of Weak Networks

If the quality of the public network is good, but the quality of the CDN is poor, according to the network measurement result, scheduling problems may occur. In that case, we can check the download link for correctness. For example, we can check whether CDN scheduling involves cross-province or cross-carrier aspects, whether the URL is hijacked, and whether CDN resources are sufficient and whether we need to enable a standby line.

3) Download Optimization of Weak Networks

If the network measurement result shows that the quality of both the public network and the CDN is poor, users are in poor network environments. In this situation, we will enable active download methods, such as concurrent downloads, QUIC, and BBR, to mitigate high latency and high packet loss rates. In addition, we will show users how to view videos in smart mode or at a lower bitrate.

4) User Scenario Profiling

Data, such as gateway latency, gateway IP address, and signal strength, perform differently in different scenarios. For example, when home networks are stable along with lower gateway latency and fixed LAN connection devices, their gateway IP addresses have something in common. We comprehensively use the previous network indicators for analysis, feature extraction, and classification. This method can be applied to the identification of final user scenarios, as shown in Figure 5.

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