bitrate.py

#!/usr/bin/env python3

import numpy as np

# A: the array with the possible last-n values
# B: the array with the calls (where each element X represents a X-peeps call)
# returns a matrix where each row "i" is a vector of how many LD streams a
# participant in  call "j" would receive.
def product(A, B):
    res = np.empty([len(A), len(B)])
    for i,row in enumerate(A):
        for j,col in enumerate(B):
            res[i, j] = np.max(np.min([row, col]), 0)
    return res

# endpoints: the array with the calls (where each element X represent a X-peeps
# call.
# last_n: the array with the possible last-n values.
# returns an array where each element contains the total bitrate going through
# the bridge for each last-n value.
def bitrate(endpoints, last_n):
    endpoint_recv_rates = endpoint_send_rates[2] + product(last_n - 1, endpoints - 2)*endpoint_send_rates[0]
    bridge_send_rate = np.matmul(endpoints, endpoint_recv_rates.T)
    bridge_recv_rate = np.sum(endpoints*np.sum(endpoint_send_rates))
    bridge_total =  bridge_recv_rate + bridge_send_rate
    return bridge_total

# we may have tweaked these a bit in meet
endpoint_send_rates = np.array([.2, .5, 2.5])

# assume a couple of calls, one 35-peeps and one 25-peeps
endpoints = np.array([35, 25])

# compute the bitrate for different last_n values
last_n = np.array([100, 9, 1])

# With these variables the bitrate method computes [688. 450. 354.] which means
# for the above two calls the (max) total bitrate will be
#
# 688Mbps with last_n 100
# 438Mbps with last_n 9
# 342Mbps with last_n 1
#
# This is the max, the actual bitrate will be more or less depending on network
# conditions and user settings (low bandwidth mode, tile-view, etc.).
print(bitrate(endpoints, last_n))